Hyne - Logs - Green and Dry
JH
- 1 Exploration
- 2 Modelling
- 3 Conclusions
- 4 Todo
1 Exploration
1.1 Dataset
1.1.1 Boards
library(lattice)
library(RODBC)
ch = odbcConnect('Hyne',uid='sa',pwd="password12")
mypanel = function(x,y,...) {
panel.abline(c(0,1), col='grey70', lty=2)
panel.xyplot(x,y,...)
}
B = sqlQuery(ch, "
select
SWILogNumber,
g.MOE [E.g],
g.Density [den.g],
m.swv [v.d.hitman],
h.[Density_kg/m3] [den.d.lhg],
h.[MOE.cltraw] [E.d.clt],
m.pith,
m.wane,
m.bow,
m.crook,
m.twist,
X_Dens [den.d.chh],
X_Clear [den.d.chh.clear],
X_DynE [E.d.chh.dyn],
X_AvgE [E.d.chh.avg],
X_MOE [E.d.chh.moe],
xc, yc
from (
select
flitchId,
max(pith) pith,
max(wane) wane,
avg(swv) swv,
avg(bow) bow,
avg(crook) crook,
avg(twist) twist
from manualMeas group by flitchId
) m
left join CHHBoards c on c.flitchId=m.flitchId
left join HyneDryBoards h on h.flitchId=m.flitchId
left join ecoustic g on m.flitchId=g.flitchId
left join (
select
flitchId,
max(SWILogNumber) SWILogNumber
from
boardEndImages i, barcode_image bi, boardEndBarcodes b
where flitchId is not null and i.id=bi.imageId and bi.barcodeId=b.id group by flitchId
) f on f.flitchId=m.flitchId
left join (
select FlitchID, avg(boardCentroidX_mm) xc, avg(boardCentroidY_mm) yc
from boardEndBarcodes b, boardEndImages i, barcode_image bi
where b.id=bi.barcodeId and i.id=bi.imageId
group by FlitchID
) xy on xy.FlitchID=m.flitchId
")
B$pith_wane = paste(B$pith,B$wane)
B$E.d.hitman = B$v.d^2*B$den.d.chh/1e9
# halve the SWV for ridiculosly fast ecoustic velocities
B$v.g = sqrt(B$E.g*1e9/B$den.g)
B$E.g.corrected = B$E.g
ii=!is.na(B$v.g) & B$v.g>4000
B$E.g.corrected[ii] = (B$v.g[ii]/2)^2*B$den.g[ii]/1e9
B$R = sqrt(B$xc^2 + B$yc^2)1202 boards. 62 not associated with a SWILogNumber.
1.1.2 Logs
L = sqlQuery(ch, "
select
p.*,
s.*,
u.SWV [SWV.untrimmed],
t.SWV [SWV],
t.weight,
3.141592654*4.9/3*(rL*rL+rS*rL+rS*rS) [volume.heart]
from
(select * from logs where SWILogNumber is not null and DateAndTime>'2014-08-31 00:00:00') p
left join (select * from logs where SWILogNumber is not null and DateAndTime<'2014-08-31 00:00:00') s
on s.SWILogNumber=p.SWILogNumber
left join yardTrimmed t on p.SWILogNumber=t.SWILogNumber
left join yardUntrimmed u on p.SWILogNumber=u.SWILogNumber
left join (select
l.SWILogNumber, l.heartwoodDiameter_mm/2000 rL, s.heartwoodDiameter_mm/2000 rS
from logends l, logends s where s.SWILogNumber=l.SWILogNumber and s.logEnd='S' and l.logEnd='L'
) h on h.SWILogNumber=p.SWILogNumber
order by p.SWILogNumber")
summ <- function(x) {
return(list(
avg=mean(x, na.rm=TRUE),
p50=median(x, na.rm=TRUE),
p75=quantile(x, 0.75, na.rm=TRUE)
))
}
board.quality.measures = c('E.d.hitman','bow','crook','twist')
for (i in 1:nrow(L)) {
ii=!is.na(B$SWILogNumber) & B$SWILogNumber==L[i,"SWILogNumber"]
L[i,"nboards"]=sum(ii)
L[i,"nboards.complete"]=nrow(na.omit(B[ii,board.quality.measures]))
L[i,"E.avg"]=mean(B$E.d.clt[ii],na.rm=TRUE)
L[i,"E.avg.dyn"]=mean(B$E.d.hitman[ii],na.rm=TRUE)
L[i,"GPa10"]=sum(!is.na(B$E.d.hitman) & B$E.d.hitman>=10 & ii)/sum(ii)
L[i,"GPa8"]=sum(!is.na(B$E.d.hitman) & B$E.d.hitman>=8 & ii)/sum(ii)
L[i,"GPa6"]=sum(!is.na(B$E.d.hitman) & B$E.d.hitman>=6 & ii)/sum(ii)
L[i,"npith"]=sum(ii & (!is.na(B$pith) & B$pith=='y'))
for (measure in board.quality.measures) {
for (region in c('','inner','outer')) {
iregion = rep(TRUE,nrow(B))
if (region=='inner') {
iregion = B$R<=100
} else if (region=='outer') {
iregion = B$R>100
}
results = summ(B[!is.na(B[,measure])&ii&iregion,measure])
for (result in names(results)) {
if (region=='') {
out <- paste(measure,result,sep="_")
} else {
out <- paste(measure,result,region,sep="_")
}
L[i,out] = results[[result]]
}
}
}
}
# calibre uses 0 to indicate missing
L$velocity[L$velocity==0]=NA
L$velocity.1[L$velocity.1==0]=NA
L$hw.vfrac = L$volume.heart / L$volume
L$density = L$weight / L$volume
L$sweep.prod = L$m_sweep1*L$m_sweep2
L$sweep.prod.1 = L$m_sweep1.1*L$m_sweep2.1
L$E.gradient = L$E.d.hitman_avg_inner/L$E.d.hitman_avg_outer
#str(L)
predictors = c('SWV',
'm_volume','m_led','m_sed',
'm_a0','m_a1','m_a2','m_taper','m_waist',
'm_ovality','m_whorliness',
'm_sweep1','m_sweep2',
'weight','density',
'hw.vfrac',
'sweep.prod')1.1.3 Log End Imagery
Also grab digitized log end data:
D = sqlQuery(ch, "
select
flipbookNumber SWILogNumber,
logEnd,
e.type,
x_mm x,
y_mm y
from
LogEndDigitizationPoints p,
LogEndDigitizationEdges e,
LogEndDigitizations d
where
p.digitizationId=d.id
and e.digitizationId=d.id
and e.id=p.edgeID")# and logEnd='large'")1.2 Cleaning
Why are the shape metrics (m_*) missing for 10 logs?
9 of these logs (198,201,208,195,205,148,194,206,207) were the first put through, maybe the logselect software wasn’t running. The 10th is a log that for some reason couldn’t be matched to Royalty scanner Id (or perhaps no bin file matching that Id was available).
Hitman SWV isn’t available for the last trimmed log (223). Can we use untrimmed hitman swv?
#plot(L$SWV,L$SWV.untrimmed*1000.)
#identify(L$SWV,L$SWV.untrimmed*1000.,L$SWILogNumber)
c=rep('grey70',nrow(L))
c[L$SWILogNumber %in% c(157,183)]='red'
c[L$SWILogNumber %in% c(151)]='blue'
c[L$SWILogNumber %in% c(223)]='green'
tmp=L[,c("velocity","SWV.untrimmed","SWV","velocity.1")]
names(tmp)<-c("calibre.select","hitman.untrm","hitman.trmd","calibre.sawing")
pairs(tmp,col=c,main="Comparison of Log SWV Measures")
Three outliers (logs 151,157,183).
Trimmed SWV estimate good for logs 157, 183 (i.e. untrimmed SWV wrong).
Use hitman untrimmed estimate for log 151.
Use hitman untrimmed estimate for log 223.
TODO: re-extract hitman SWV from raw hitman data.
L$SWV[L$SWILogNumber==151] = L$SWV.untrimmed[L$SWILogNumber==151]*1000
L$SWV[L$SWILogNumber==223] = L$SWV.untrimmed[L$SWILogNumber==223]*1000Do the two measures of volume/LED/SED agree?
plot(volume ~ m_volume, L)
plot(SED ~ m_sed, L)
plot(LED ~ m_led, L)
Yep, pretty much.
Is the heartwood volume stuff sane?
xyplot(volume.heart ~ volume, L)
xyplot(I(volume.heart/volume) ~ I(weight/volume), L)
summary(lm(I(weight/volume) ~ I(volume.heart/volume), L))##
## Call:
## lm(formula = I(weight/volume) ~ I(volume.heart/volume), data = L)
##
## Residuals:
## Min 1Q Median 3Q Max
## -105.612 -23.525 -5.409 19.012 130.911
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1058.725 8.902 118.93 <2e-16 ***
## I(volume.heart/volume) -520.867 34.059 -15.29 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 39.78 on 121 degrees of freedom
## Multiple R-squared: 0.659, Adjusted R-squared: 0.6562
## F-statistic: 233.9 on 1 and 121 DF, p-value: < 2.2e-16Regression suggests average sapwood density of 1058 kg/m^3, with average heartwood density of 537 kg/m^3.
How different are log metrics computed at selection and processing time (i.e. before and after being trimmed to 4.9m)?
par(mfcol=c(4,4))
log.shape.metrics = c("m_volume","m_led","m_sed","m_taper","m_waist","m_ovality","m_whorliness","m_a0","m_a1","m_a2","m_sweep1","m_sweep2","sweep.prod")
for (m in log.shape.metrics) {
plot(formula(paste(m,"~",m,".1",sep="")),L)
}
For the most part log metrics are similar before and after trimming. Where differences do occur it might be due to:
- the loss of material
- discretization error (incl slight difference in positioning)
- instrument error
1.3 Recovery
plot(nboards ~ m_volume, L)
identify(L$m_volume, L$nboards, L$SWILogNumber, cex=0.8)
## integer(0)Only one board recovered from log 106.
Two logs with abnormally poor recovery: 126, 192. Either these were sawn to non-90x40 products or gluing was poor and labels were lost.
Exclude these logs from further analysis.
#L = L[!L$SWILogNumber%in%c(106,126,192),]1.4 Stiffness Out-turn
bwplot( factor(SWILogNumber,levels=L$SWILogNumber[order(L$E.avg)],ordered=TRUE) ~ E.d.clt, B)
# and again but with only the best and worst and with individual boards
(worst=L$SWILogNumber[L$E.avg<quantile(L$E.avg,0.1)])## [1] 101 106 163 168 179 184 187 193 205 207 214 218 221(best=L$SWILogNumber[L$E.avg>quantile(L$E.avg,0.9)])## [1] 135 137 140 143 154 159 176 190 200 203 206 213 216bwplot( factor(SWILogNumber,levels=L$SWILogNumber[order(L$E.avg)],ordered=TRUE) ~ E.d.clt, B,subset=SWILogNumber %in% union(best,worst),
panel=function(...){
panel.bwplot(...)
panel.points(...,col='red')
}, main="Best and Worst 10% of Logs")
The worst (least stiff) boards are similar across all logs. The best (most stiff) boards howver range from <10 GPa to >15GPa.
Are good logs small?
plot(L$m_volume[order(L$E.avg)])
plot(L$nboards[order(L$E.avg)])
Bad logs tend to be both a little smaller and represented by fewer boards, but the effect is not strong.
1.5 Grade
Note that ‘GPax’ here denotes boards whose average MOE is x GPa or better, which is not really the same as having a GPax grade.
par(mfcol=c(3,1))
hist(L$GPa6, xlim=c(0,1), breaks=c(0:11)/10)
hist(L$GPa8, xlim=c(0,1), breaks=c(0:11)/10)
hist(L$GPa10, xlim=c(0,1), breaks=c(0:11)/10)
xyplot(jitter(L$GPa8,5)~jitter(L$GPa10,5))
Interesting that the 5 best logs if seeking GPa8 include one log that would be in the lower half if you were looking for GPa10.
par(mfcol=c(3,1))
plot(GPa6~ E.avg.dyn, L)
plot(GPa8~ E.avg.dyn, L)
plot(GPa10~ E.avg.dyn, L)
Log average MOE is not necessarily a good predictor of fraction of boards exceeding a particular MOE limit.
1.6 Marco’s Proposal Statement
How does the fraction GPa10+ vary with log quality? I.e. what is the % of GPa10+ in the worst X% of logs?
par(mfcol=c(1,1))
F=ecdf(L$GPa6)
plot(F)
quantile(L$GPa6, 0.05)## 5%
## 0.5038462quantile(L$GPa6, 0.10)## 10%
## 0.5866667TODO: using a model for log stiffness based on pre-sawing measures, redo this.
1.7 Dynamic v Static
Does it matter if we use Hitman instead of CLT results?
1.7.1 Using hitman and CHH density
bwplot( factor(SWILogNumber,levels=L$SWILogNumber[order(L$E.avg.dyn)],ordered=TRUE) ~ E.d.hitman, B)
# and again but with only the best and worst and with individual boards
(worst.dyn=L$SWILogNumber[L$E.avg<quantile(L$E.avg,0.1)])## [1] 101 106 163 168 179 184 187 193 205 207 214 218 221(best.dyn=L$SWILogNumber[L$E.avg>quantile(L$E.avg,0.9)])## [1] 135 137 140 143 154 159 176 190 200 203 206 213 216bwplot( factor(SWILogNumber,levels=L$SWILogNumber[order(L$E.avg)],ordered=TRUE) ~ E.d.hitman, B,subset=SWILogNumber %in% union(best.dyn,worst.dyn),
panel=function(...){
panel.bwplot(...)
panel.points(...,col='red')
}, main="Best and Worst 10% of logs")
intersect(best, best.dyn)## [1] 135 137 140 143 154 159 176 190 200 203 206 213 216intersect(worst, worst.dyn)## [1] 101 106 163 168 179 184 187 193 205 207 214 218 221plot(L$E.avg,L$E.avg.dyn)
plot(rank(L$E.avg),rank(L$E.avg.dyn))
No, we get the same sets of logs as best and worst using either hitman+chh.density or clt for board MOE.
From here on use Hitman and CHH density based board and log average MOE.
1.8 Pith Boards
Do all logs have a similar number of pith-in boards?
plot(L$SWILogNumber, L$nboards, ylim=c(0,max(L$nboards)))
points(L$SWILogNumber, L$npith, col='red')
table(L$npith)##
## 0 1 2 3 4
## 7 43 59 13 1L$SWILogNumber[L$npith==0]## [1] 106 115 125 126 147 175 219Typically 1 or 2 pith boards per log. BUT 7 logs with no pith boards (either not recovered or not properly classified), this might skew which logs appear to be best and worst. One of these seven is also a worst 10% log so probably not an issue.
Is the fraction of pith boards a good predictor of log average stiffness?
xyplot(E.avg.dyn ~ I(npith/nboards), L)
No.
1.9 Board Densities
1.9.1 Different estimators of dry density
pairs(B[,c('den.d.chh','den.d.chh.clear','den.d.lhg'),])
CHH clear (X_Clear) and average (X_Dens) densities are pretty similar.
Use the latter (X_Dens) in conjunction with SWV to estimate MOE.
1.9.2 Green vs dry density
xyplot(den.d.chh ~ den.g, B, group=paste("wane =",wane), auto.key=TRUE)
Most of the incredibly high green density boards are waney. Probably the green mass is good, but the green volume
1.10 Variation with Distance-from-pith
plot(pith ~ R, B)
Wow. There are pith containing boards whose LE position is 140-160 mm from pith at LE.
1.10.1 Density
mypanel = function(...) {
panel.xyplot(...)
panel.grid(h=-1,v=-1)
panel.loess(..., col='red')
}
library(gridExtra)## Loading required package: gridlibrary(lattice)
grid.arrange(
xyplot(den.g ~ R, B, panel=mypanel, group=wane),
xyplot(den.d.chh ~ R, B, panel=mypanel, group=wane),
xyplot(den.d.lhg ~ R, B, panel=mypanel, group=wane),
ncol=3)
1.10.2 MOE
grid.arrange(
xyplot(E.g.corrected ~ R, B, panel=mypanel, group=wane),
xyplot(E.d.chh.dyn ~ R, B, panel=mypanel, group=wane),
xyplot(E.d.hitman ~ R, B, panel=mypanel, group=wane),
ncol=3)
1.10.3 Warp
grid.arrange(
xyplot(bow ~ R, B, panel=mypanel, group=wane),
xyplot(crook ~ R, B, panel=mypanel, group=wane),
xyplot(twist ~ R, B, panel=mypanel, group=wane),
ncol=3)
Most of the plots above exhibit a slope discontinuity between \(R=100\) and \(R=150\) mm.
Lets call boards with \(R<100\) ‘inner’ and those with \(R>100\) mm ‘outer’.
1.11 Intra-Log Variation
1.11.1 Radial
Plot variation of moe, den, bow, crook, twist with R within individual logs
for (p in c("den.g","den.d.chh","E.d.chh.dyn","bow","crook","twist")) {
print(
xyplot(formula(paste(p,"~ R | as.factor(SWILogNumber)")), B, group=pith_wane, pch=c(1,19,19,19), main=p)#, auto.key=TRUE)
)
}
Some very odd arrangements of pith and waney boards (e.g log 137 where a pith board has R > than a wane board!)
1.11.2 2D
Todo: plot moe, den, bow, crook, twist using glyph size/color/fill over individual log saw patterns
library(ggplot2)
ii = is.finite(B$SWILogNumber) & is.finite(B$xc) & is.finite(B$yc)
for (p in c("den.g","den.d.chh","E.d.chh.dyn","bow","crook","twist")) {
#B$sf=B[,p]/max(B[,p])
#xyplot(yc ~ xc | as.factor(SWILogNumber), aspect='iso', B, cex=B$sf, pch=19, subset=is.finite(p))#, cex=p)#, auto.key=TRUE)
# try ggplot2
B$size=B[,p]
print(
ggplot(B[is.finite(B$size) & ii,], aes(xc,yc))
+ geom_point(aes(size=sqrt(abs(size)),col=size,alpha=0.9))
+ geom_point(col='black', shape="+")
+ facet_wrap(~SWILogNumber)
+ coord_fixed() # achieves aspect='iso'
+ scale_colour_gradientn(colours=rainbow(4))
+ ggtitle(p)
)
}
# toWhich logs are well represented?
Plot board positions and digitized large end information.
xyplot(y ~ x | as.factor(SWILogNumber), group=paste(logEnd,type), D, type="l", aspect='iso',
panel=function(x,y,subscripts,...){
panel.grid(h=-1,y=-1)
log = D$SWILogNumber[subscripts][1]
ii = B$SWILogNumber==log
panel.xyplot(B$xc[ii],B$yc[ii],pch=19,col='black', cex=0.5)
panel.xyplot(x,y,subscripts=subscripts,...)
})
# and so after much scrutinising...
near.complete.sawpatterns = c(213,217,219,220,221,223,212,210,108,111,125,119,114,128,144,150,154,164,188,186,197)How is it that some boards fall outside of the large end? e.g. 103. Do I have the rotations correct?
1.12 Log SWV v average Board MOE
mypanel=function(x,y,...){
#panel.abline(c(0,1), col='grey70', lty=2)
panel.xyplot(x,y,...)
m=lm(y ~ x)
print(summary(m))
panel.abline(coef(m),col='red')
}
#xyplot(E.avg ~ SWV, L, panel=mypanel) # hitman in yard
#xyplot(E.avg ~ velocity, L, subset=velocity>0, panel=mypanel) # calibre log tool
xyplot(E.avg.dyn ~ SWV, L, panel=mypanel) # hitman in yard
##
## Call:
## lm(formula = y ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5226 -0.6399 0.0464 0.7224 2.8393
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.5446846 1.4600138 -3.113 0.00231 **
## x 0.0041265 0.0004422 9.331 6.33e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.196 on 121 degrees of freedom
## Multiple R-squared: 0.4184, Adjusted R-squared: 0.4136
## F-statistic: 87.06 on 1 and 121 DF, p-value: 6.332e-16#xyplot(E.avg.dyn ~ velocity, L, subset=velocity>0, panel=mypanel) # calibre log toolUgly.
1.13 Warp
1.13.1 Per Log Distributions
bwpanel=function(...){
panel.bwplot(...)
panel.points(...,col='red', cex=0.3)
}
bwplot( factor(SWILogNumber,levels=L$SWILogNumber[order(L$bow_p75)],ordered=TRUE) ~ bow, B, panel=bwpanel)
bwplot( factor(SWILogNumber,levels=L$SWILogNumber[order(L$crook_p75)],ordered=TRUE) ~ crook, B, panel=bwpanel)
bwplot( factor(SWILogNumber,levels=L$SWILogNumber[order(L$twist_p75)],ordered=TRUE) ~ twist, B, panel=bwpanel)
1.13.2 Correlations
1.13.2.1 Between Log Quality Measures
log.quality.measures = c("E.avg.dyn",
"crook_avg","crook_p75","crook_avg_inner","crook_avg_outer",
"bow_avg","bow_p75","bow_avg_outer","bow_avg_inner",
"twist_avg")
pairs(L[,log.quality.measures],
lower.panel=function(x,y,...) {
usr <- par("usr"); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
ii=is.finite(x) & is.finite(y)
r <- cor(x[ii], y[ii])
txt <- format(c(r, 0.123456789), digits = 2)[1]
#txt <- paste0("r=", txt)
#cex.cor <- 0.8/strwidth(txt)
text(0.5, 0.5, txt, cex = 1.8 * abs(r))
})
Average and 75th percentile bow and crook strongly correlated. Consider only average from here on.
1.13.2.2 Log Q vs Predictors
log.quality.measures = c("E.avg.dyn",
"crook_avg","crook_avg_inner","crook_avg_outer",
"bow_avg","bow_avg_outer","bow_avg_inner",
"twist_avg")
plots <- list()
for (p in predictors) {
for (l in log.quality.measures) {
plots <- c(plots, list(xyplot(L[,l] ~ L[,p], xlab=p, ylab=l)))
}
}
do.call(grid.arrange, c(plots, ncol=length(predictors)))
Twist turns out to be surprisingly predictable (see below). Is this due to correlation with stiffness?
xyplot(E.d.hitman_avg ~ twist_avg, L)
summary(lm(E.d.hitman_avg ~ twist_avg, L))##
## Call:
## lm(formula = E.d.hitman_avg ~ twist_avg, data = L)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7408 -0.9521 -0.0143 0.9708 4.0205
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.09563 0.22422 45.026 < 2e-16 ***
## twist_avg -0.34252 0.06032 -5.679 9.5e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 121 degrees of freedom
## Multiple R-squared: 0.2104, Adjusted R-squared: 0.2039
## F-statistic: 32.25 on 1 and 121 DF, p-value: 9.503e-082 Modelling
2.1 Stiffness
Find the best linear model for log average MOE.
summary(m <- lm(E.avg.dyn ~ (m_volume+SWV+m_led+m_sed+m_a0+m_a1+m_a2+m_taper+m_waist+m_ovality+m_whorliness+m_sweep1+m_sweep2+weight+hw.vfrac), L, subset=!is.na(L$m_sed)))##
## Call:
## lm(formula = E.avg.dyn ~ (m_volume + SWV + m_led + m_sed + m_a0 +
## m_a1 + m_a2 + m_taper + m_waist + m_ovality + m_whorliness +
## m_sweep1 + m_sweep2 + weight + hw.vfrac), data = L, subset = !is.na(L$m_sed))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2389 -0.4317 0.0096 0.4803 1.5686
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.534e+00 2.002e+00 -2.765 0.00682 **
## m_volume -8.647e+00 6.083e+00 -1.421 0.15839
## SWV 4.817e-03 3.877e-04 12.425 < 2e-16 ***
## m_led 3.582e-02 1.557e-02 2.300 0.02360 *
## m_sed -4.029e-03 1.444e-02 -0.279 0.78085
## m_a0 -3.275e-02 2.357e-02 -1.389 0.16791
## m_a1 8.202e-02 1.606e-01 0.511 0.61071
## m_a2 1.584e-01 1.605e-01 0.987 0.32627
## m_taper -5.622e-01 7.566e-01 -0.743 0.45923
## m_waist -6.372e+00 1.095e+01 -0.582 0.56179
## m_ovality 7.277e+01 1.150e+02 0.633 0.52844
## m_whorliness -4.602e-01 3.015e-01 -1.527 0.13012
## m_sweep1 3.305e-02 1.340e-01 0.247 0.80571
## m_sweep2 -2.303e-01 9.123e-01 -0.252 0.80126
## weight 9.852e-03 5.164e-03 1.908 0.05936 .
## hw.vfrac -4.836e+00 1.302e+00 -3.714 0.00034 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8017 on 97 degrees of freedom
## Multiple R-squared: 0.7538, Adjusted R-squared: 0.7158
## F-statistic: 19.8 on 15 and 97 DF, p-value: < 2.2e-16summary(m.best <- step(m, direction="both"))## Start: AIC=-35.19
## E.avg.dyn ~ (m_volume + SWV + m_led + m_sed + m_a0 + m_a1 + m_a2 +
## m_taper + m_waist + m_ovality + m_whorliness + m_sweep1 +
## m_sweep2 + weight + hw.vfrac)
##
## Df Sum of Sq RSS AIC
## - m_sweep1 1 0.039 62.389 -37.122
## - m_sweep2 1 0.041 62.391 -37.119
## - m_sed 1 0.050 62.400 -37.102
## - m_a1 1 0.168 62.517 -36.889
## - m_waist 1 0.218 62.568 -36.799
## - m_ovality 1 0.257 62.607 -36.728
## - m_taper 1 0.355 62.705 -36.551
## - m_a2 1 0.626 62.976 -36.064
## <none> 62.350 -35.193
## - m_a0 1 1.241 63.591 -34.966
## - m_volume 1 1.299 63.649 -34.863
## - m_whorliness 1 1.498 63.848 -34.510
## - weight 1 2.340 64.690 -33.030
## - m_led 1 3.400 65.750 -31.193
## - hw.vfrac 1 8.868 71.218 -22.166
## - SWV 1 99.227 161.577 70.408
##
## Step: AIC=-37.12
## E.avg.dyn ~ m_volume + SWV + m_led + m_sed + m_a0 + m_a1 + m_a2 +
## m_taper + m_waist + m_ovality + m_whorliness + m_sweep2 +
## weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## - m_sweep2 1 0.023 62.412 -39.081
## - m_sed 1 0.048 62.437 -39.034
## - m_a1 1 0.185 62.574 -38.787
## - m_waist 1 0.224 62.613 -38.717
## - m_ovality 1 0.256 62.645 -38.660
## - m_taper 1 0.382 62.771 -38.433
## - m_a2 1 0.674 63.063 -37.908
## <none> 62.389 -37.122
## - m_a0 1 1.232 63.621 -36.912
## - m_volume 1 1.424 63.813 -36.572
## - m_whorliness 1 1.499 63.888 -36.440
## + m_sweep1 1 0.039 62.350 -35.193
## - weight 1 2.405 64.794 -34.848
## - m_led 1 3.455 65.844 -33.032
## - hw.vfrac 1 8.859 71.248 -24.118
## - SWV 1 99.191 161.580 68.410
##
## Step: AIC=-39.08
## E.avg.dyn ~ m_volume + SWV + m_led + m_sed + m_a0 + m_a1 + m_a2 +
## m_taper + m_waist + m_ovality + m_whorliness + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## - m_sed 1 0.062 62.474 -40.968
## - m_a1 1 0.207 62.619 -40.706
## - m_waist 1 0.230 62.641 -40.666
## - m_ovality 1 0.257 62.668 -40.617
## - m_taper 1 0.413 62.825 -40.335
## - m_a2 1 0.733 63.145 -39.761
## <none> 62.412 -39.081
## - m_a0 1 1.211 63.622 -38.910
## - m_volume 1 1.602 64.014 -38.216
## - m_whorliness 1 1.832 64.244 -37.812
## + m_sweep2 1 0.023 62.389 -37.122
## + m_sweep1 1 0.021 62.391 -37.119
## - weight 1 2.642 65.053 -36.396
## - m_led 1 3.436 65.848 -35.025
## - hw.vfrac 1 8.945 71.357 -25.945
## - SWV 1 105.402 167.814 70.688
##
## Step: AIC=-40.97
## E.avg.dyn ~ m_volume + SWV + m_led + m_a0 + m_a1 + m_a2 + m_taper +
## m_waist + m_ovality + m_whorliness + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## - m_a1 1 0.192 62.666 -42.621
## - m_waist 1 0.222 62.696 -42.567
## - m_ovality 1 0.257 62.731 -42.504
## - m_taper 1 0.394 62.868 -42.257
## - m_a2 1 0.701 63.175 -41.708
## <none> 62.474 -40.968
## - m_volume 1 1.543 64.017 -40.211
## - m_whorliness 1 1.769 64.244 -39.812
## + m_sed 1 0.062 62.412 -39.081
## + m_sweep2 1 0.037 62.437 -39.034
## + m_sweep1 1 0.016 62.458 -38.997
## - m_a0 1 2.445 64.919 -38.630
## - weight 1 2.586 65.060 -38.385
## - m_led 1 3.440 65.914 -36.912
## - hw.vfrac 1 9.528 72.002 -26.928
## - SWV 1 105.559 168.033 68.835
##
## Step: AIC=-42.62
## E.avg.dyn ~ m_volume + SWV + m_led + m_a0 + m_a2 + m_taper +
## m_waist + m_ovality + m_whorliness + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## - m_ovality 1 0.267 62.933 -44.141
## - m_waist 1 0.483 63.149 -43.754
## - m_a2 1 0.557 63.223 -43.622
## <none> 62.666 -42.621
## - m_volume 1 1.357 64.024 -42.199
## - m_whorliness 1 1.672 64.338 -41.646
## + m_a1 1 0.192 62.474 -40.968
## + m_sweep2 1 0.060 62.607 -40.729
## + m_sed 1 0.047 62.619 -40.706
## + m_sweep1 1 0.023 62.643 -40.663
## - weight 1 2.402 65.068 -40.371
## - m_a0 1 2.663 65.329 -39.919
## - m_taper 1 2.867 65.534 -39.565
## - m_led 1 3.550 66.216 -38.395
## - hw.vfrac 1 10.281 72.947 -27.455
## - SWV 1 109.017 171.683 69.264
##
## Step: AIC=-44.14
## E.avg.dyn ~ m_volume + SWV + m_led + m_a0 + m_a2 + m_taper +
## m_waist + m_whorliness + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## - m_waist 1 0.494 63.427 -45.257
## - m_a2 1 0.565 63.498 -45.131
## <none> 62.933 -44.141
## - m_volume 1 1.381 64.314 -43.688
## + m_ovality 1 0.267 62.666 -42.621
## - m_whorliness 1 2.000 64.933 -42.605
## + m_a1 1 0.202 62.731 -42.504
## + m_sweep2 1 0.062 62.871 -42.252
## + m_sed 1 0.047 62.886 -42.226
## + m_sweep1 1 0.022 62.911 -42.180
## - weight 1 2.348 65.281 -42.002
## - m_a0 1 2.805 65.738 -41.213
## - m_taper 1 3.013 65.946 -40.857
## - m_led 1 3.918 66.851 -39.317
## - hw.vfrac 1 10.227 73.160 -29.125
## - SWV 1 109.242 172.175 67.587
##
## Step: AIC=-45.26
## E.avg.dyn ~ m_volume + SWV + m_led + m_a0 + m_a2 + m_taper +
## m_whorliness + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## <none> 63.427 -45.257
## - m_volume 1 1.491 64.918 -44.632
## + m_waist 1 0.494 62.933 -44.141
## + m_a1 1 0.471 62.956 -44.099
## + m_ovality 1 0.278 63.149 -43.754
## - m_whorliness 1 2.107 65.535 -43.564
## + m_sweep2 1 0.094 63.333 -43.424
## + m_sweep1 1 0.033 63.394 -43.317
## + m_sed 1 0.029 63.398 -43.309
## - weight 1 2.466 65.893 -42.947
## - m_a2 1 2.934 66.361 -42.148
## - m_a0 1 3.427 66.854 -41.310
## - m_taper 1 3.526 66.953 -41.143
## - m_led 1 4.912 68.339 -38.828
## - hw.vfrac 1 9.973 73.400 -30.755
## - SWV 1 109.227 172.654 65.901##
## Call:
## lm(formula = E.avg.dyn ~ m_volume + SWV + m_led + m_a0 + m_a2 +
## m_taper + m_whorliness + weight + hw.vfrac, data = L, subset = !is.na(L$m_sed))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2835 -0.3965 -0.0057 0.5262 1.6484
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.7164370 1.8521928 -3.086 0.002603 **
## m_volume -8.3559340 5.3706048 -1.556 0.122808
## SWV 0.0048570 0.0003647 13.318 < 2e-16 ***
## m_led 0.0411388 0.0145659 2.824 0.005690 **
## m_a0 -0.0415803 0.0176248 -2.359 0.020200 *
## m_a2 0.0067362 0.0030861 2.183 0.031327 *
## m_taper -0.1951795 0.0815625 -2.393 0.018522 *
## m_whorliness -0.4933860 0.2667003 -1.850 0.067185 .
## weight 0.0093774 0.0046857 2.001 0.047995 *
## hw.vfrac -4.8580360 1.2071640 -4.024 0.000109 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7847 on 103 degrees of freedom
## Multiple R-squared: 0.7496, Adjusted R-squared: 0.7277
## F-statistic: 34.25 on 9 and 103 DF, p-value: < 2.2e-16m.best$anova## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 NA NA 97 62.34979 -35.19291
## 2 - m_sweep1 1 0.03910209 98 62.38889 -37.12207
## 3 - m_sweep2 1 0.02286265 99 62.41175 -39.08067
## 4 - m_sed 1 0.06232582 100 62.47408 -40.96788
## 5 - m_a1 1 0.19208366 101 62.66616 -42.62098
## 6 - m_ovality 1 0.26676899 102 62.93293 -44.14096
## 7 - m_waist 1 0.49409360 103 63.42702 -45.25725m.best <- step(m, direction="backward")## Start: AIC=-35.19
## E.avg.dyn ~ (m_volume + SWV + m_led + m_sed + m_a0 + m_a1 + m_a2 +
## m_taper + m_waist + m_ovality + m_whorliness + m_sweep1 +
## m_sweep2 + weight + hw.vfrac)
##
## Df Sum of Sq RSS AIC
## - m_sweep1 1 0.039 62.389 -37.122
## - m_sweep2 1 0.041 62.391 -37.119
## - m_sed 1 0.050 62.400 -37.102
## - m_a1 1 0.168 62.517 -36.889
## - m_waist 1 0.218 62.568 -36.799
## - m_ovality 1 0.257 62.607 -36.728
## - m_taper 1 0.355 62.705 -36.551
## - m_a2 1 0.626 62.976 -36.064
## <none> 62.350 -35.193
## - m_a0 1 1.241 63.591 -34.966
## - m_volume 1 1.299 63.649 -34.863
## - m_whorliness 1 1.498 63.848 -34.510
## - weight 1 2.340 64.690 -33.030
## - m_led 1 3.400 65.750 -31.193
## - hw.vfrac 1 8.868 71.218 -22.166
## - SWV 1 99.227 161.577 70.408
##
## Step: AIC=-37.12
## E.avg.dyn ~ m_volume + SWV + m_led + m_sed + m_a0 + m_a1 + m_a2 +
## m_taper + m_waist + m_ovality + m_whorliness + m_sweep2 +
## weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## - m_sweep2 1 0.023 62.412 -39.081
## - m_sed 1 0.048 62.437 -39.034
## - m_a1 1 0.185 62.574 -38.787
## - m_waist 1 0.224 62.613 -38.717
## - m_ovality 1 0.256 62.645 -38.660
## - m_taper 1 0.382 62.771 -38.433
## - m_a2 1 0.674 63.063 -37.908
## <none> 62.389 -37.122
## - m_a0 1 1.232 63.621 -36.912
## - m_volume 1 1.424 63.813 -36.572
## - m_whorliness 1 1.499 63.888 -36.440
## - weight 1 2.405 64.794 -34.848
## - m_led 1 3.455 65.844 -33.032
## - hw.vfrac 1 8.859 71.248 -24.118
## - SWV 1 99.191 161.580 68.410
##
## Step: AIC=-39.08
## E.avg.dyn ~ m_volume + SWV + m_led + m_sed + m_a0 + m_a1 + m_a2 +
## m_taper + m_waist + m_ovality + m_whorliness + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## - m_sed 1 0.062 62.474 -40.968
## - m_a1 1 0.207 62.619 -40.706
## - m_waist 1 0.230 62.641 -40.666
## - m_ovality 1 0.257 62.668 -40.617
## - m_taper 1 0.413 62.825 -40.335
## - m_a2 1 0.733 63.145 -39.761
## <none> 62.412 -39.081
## - m_a0 1 1.211 63.622 -38.910
## - m_volume 1 1.602 64.014 -38.216
## - m_whorliness 1 1.832 64.244 -37.812
## - weight 1 2.642 65.053 -36.396
## - m_led 1 3.436 65.848 -35.025
## - hw.vfrac 1 8.945 71.357 -25.945
## - SWV 1 105.402 167.814 70.688
##
## Step: AIC=-40.97
## E.avg.dyn ~ m_volume + SWV + m_led + m_a0 + m_a1 + m_a2 + m_taper +
## m_waist + m_ovality + m_whorliness + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## - m_a1 1 0.192 62.666 -42.621
## - m_waist 1 0.222 62.696 -42.567
## - m_ovality 1 0.257 62.731 -42.504
## - m_taper 1 0.394 62.868 -42.257
## - m_a2 1 0.701 63.175 -41.708
## <none> 62.474 -40.968
## - m_volume 1 1.543 64.017 -40.211
## - m_whorliness 1 1.769 64.244 -39.812
## - m_a0 1 2.445 64.919 -38.630
## - weight 1 2.586 65.060 -38.385
## - m_led 1 3.440 65.914 -36.912
## - hw.vfrac 1 9.528 72.002 -26.928
## - SWV 1 105.559 168.033 68.835
##
## Step: AIC=-42.62
## E.avg.dyn ~ m_volume + SWV + m_led + m_a0 + m_a2 + m_taper +
## m_waist + m_ovality + m_whorliness + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## - m_ovality 1 0.267 62.933 -44.141
## - m_waist 1 0.483 63.149 -43.754
## - m_a2 1 0.557 63.223 -43.622
## <none> 62.666 -42.621
## - m_volume 1 1.357 64.024 -42.199
## - m_whorliness 1 1.672 64.338 -41.646
## - weight 1 2.402 65.068 -40.371
## - m_a0 1 2.663 65.329 -39.919
## - m_taper 1 2.867 65.534 -39.565
## - m_led 1 3.550 66.216 -38.395
## - hw.vfrac 1 10.281 72.947 -27.455
## - SWV 1 109.017 171.683 69.264
##
## Step: AIC=-44.14
## E.avg.dyn ~ m_volume + SWV + m_led + m_a0 + m_a2 + m_taper +
## m_waist + m_whorliness + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## - m_waist 1 0.494 63.427 -45.257
## - m_a2 1 0.565 63.498 -45.131
## <none> 62.933 -44.141
## - m_volume 1 1.381 64.314 -43.688
## - m_whorliness 1 2.000 64.933 -42.605
## - weight 1 2.348 65.281 -42.002
## - m_a0 1 2.805 65.738 -41.213
## - m_taper 1 3.013 65.946 -40.857
## - m_led 1 3.918 66.851 -39.317
## - hw.vfrac 1 10.227 73.160 -29.125
## - SWV 1 109.242 172.175 67.587
##
## Step: AIC=-45.26
## E.avg.dyn ~ m_volume + SWV + m_led + m_a0 + m_a2 + m_taper +
## m_whorliness + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## <none> 63.427 -45.257
## - m_volume 1 1.491 64.918 -44.632
## - m_whorliness 1 2.107 65.535 -43.564
## - weight 1 2.466 65.893 -42.947
## - m_a2 1 2.934 66.361 -42.148
## - m_a0 1 3.427 66.854 -41.310
## - m_taper 1 3.526 66.953 -41.143
## - m_led 1 4.912 68.339 -38.828
## - hw.vfrac 1 9.973 73.400 -30.755
## - SWV 1 109.227 172.654 65.901summary(m.best <- step(lm(E.avg.dyn ~ SWV, L, subset=!is.na(L$m_sed)), direction="forward", scope=E.avg.dyn ~ (SWV+m_volume+m_led+m_sed+m_a0+m_a1+m_a2+m_taper+m_waist+m_ovality+m_whorliness+m_sweep1+m_sweep2+volume+weight+density+hw.vfrac)))## Start: AIC=32.16
## E.avg.dyn ~ SWV
##
## Df Sum of Sq RSS AIC
## + density 1 63.066 81.910 -30.359
## + hw.vfrac 1 54.965 90.011 -19.703
## + weight 1 22.231 122.745 15.348
## + m_led 1 15.832 129.144 21.090
## + volume 1 14.732 130.244 22.049
## + m_volume 1 14.727 130.249 22.053
## + m_a0 1 14.526 130.450 22.227
## + m_sed 1 14.378 130.598 22.356
## + m_a2 1 12.149 132.828 24.268
## + m_waist 1 12.043 132.933 24.357
## + m_a1 1 9.069 135.907 26.858
## + m_whorliness 1 8.582 136.395 27.263
## + m_sweep2 1 3.763 141.213 31.186
## <none> 144.976 32.158
## + m_taper 1 1.836 143.140 32.718
## + m_ovality 1 0.545 144.431 33.732
## + m_sweep1 1 0.276 144.700 33.942
##
## Step: AIC=-30.36
## E.avg.dyn ~ SWV + density
##
## Df Sum of Sq RSS AIC
## + m_a2 1 7.2599 74.650 -38.847
## + m_waist 1 7.2479 74.662 -38.828
## + m_led 1 7.1454 74.765 -38.673
## + weight 1 6.7086 75.202 -38.015
## + volume 1 6.6960 75.214 -37.996
## + m_volume 1 6.6873 75.223 -37.983
## + m_a0 1 6.6070 75.303 -37.863
## + m_sed 1 6.3559 75.554 -37.486
## + m_a1 1 6.0087 75.902 -36.968
## + m_whorliness 1 3.6326 78.278 -33.485
## + hw.vfrac 1 3.1040 78.806 -32.725
## <none> 81.910 -30.359
## + m_taper 1 0.6047 81.306 -29.197
## + m_ovality 1 0.4056 81.505 -28.920
## + m_sweep2 1 0.1478 81.763 -28.563
## + m_sweep1 1 0.0121 81.898 -28.376
##
## Step: AIC=-38.85
## E.avg.dyn ~ SWV + density + m_a2
##
## Df Sum of Sq RSS AIC
## + hw.vfrac 1 5.4624 69.188 -45.433
## + weight 1 3.5296 71.121 -42.320
## + m_volume 1 3.5154 71.135 -42.297
## + volume 1 3.5154 71.135 -42.297
## + m_sed 1 3.1615 71.489 -41.737
## + m_a0 1 3.1573 71.493 -41.730
## + m_led 1 3.1354 71.515 -41.695
## + m_whorliness 1 3.1338 71.517 -41.693
## <none> 74.650 -38.847
## + m_ovality 1 0.6438 74.007 -37.825
## + m_sweep2 1 0.1350 74.515 -37.051
## + m_sweep1 1 0.1048 74.546 -37.005
## + m_a1 1 0.0044 74.646 -36.853
## + m_taper 1 0.0043 74.646 -36.853
## + m_waist 1 0.0035 74.647 -36.852
##
## Step: AIC=-45.43
## E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac
##
## Df Sum of Sq RSS AIC
## + m_led 1 2.64503 66.543 -47.838
## + m_volume 1 2.59123 66.597 -47.747
## + volume 1 2.55412 66.634 -47.684
## + weight 1 2.52886 66.659 -47.641
## + m_sed 1 2.51511 66.673 -47.618
## + m_a0 1 2.46386 66.724 -47.531
## + m_whorliness 1 2.00495 67.183 -46.756
## <none> 69.188 -45.433
## + m_ovality 1 1.02793 68.160 -45.125
## + m_sweep2 1 0.36878 68.819 -44.037
## + m_waist 1 0.36309 68.825 -44.028
## + m_a1 1 0.00079 69.187 -43.435
## + m_sweep1 1 0.00056 69.187 -43.434
## + m_taper 1 0.00028 69.188 -43.434
##
## Step: AIC=-47.84
## E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led
##
## Df Sum of Sq RSS AIC
## + m_whorliness 1 3.4686 63.074 -51.887
## <none> 66.543 -47.838
## + m_ovality 1 0.6808 65.862 -47.000
## + m_waist 1 0.5291 66.014 -46.740
## + m_sweep2 1 0.3435 66.200 -46.423
## + m_taper 1 0.2110 66.332 -46.197
## + m_a1 1 0.2024 66.341 -46.182
## + m_a0 1 0.0110 66.532 -45.857
## + m_volume 1 0.0059 66.537 -45.848
## + m_sweep1 1 0.0008 66.542 -45.839
## + m_sed 1 0.0002 66.543 -45.838
## + volume 1 0.0002 66.543 -45.838
## + weight 1 0.0001 66.543 -45.838
##
## Step: AIC=-51.89
## E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness
##
## Df Sum of Sq RSS AIC
## <none> 63.074 -51.887
## + m_waist 1 0.46288 62.611 -50.720
## + m_sed 1 0.31475 62.760 -50.453
## + weight 1 0.29155 62.783 -50.411
## + m_ovality 1 0.28823 62.786 -50.405
## + m_a0 1 0.26551 62.809 -50.364
## + volume 1 0.23528 62.839 -50.310
## + m_volume 1 0.11511 62.959 -50.094
## + m_sweep1 1 0.06520 63.009 -50.004
## + m_sweep2 1 0.00538 63.069 -49.897
## + m_taper 1 0.00252 63.072 -49.892
## + m_a1 1 0.00116 63.073 -49.889##
## Call:
## lm(formula = E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led +
## m_whorliness, data = L, subset = !is.na(L$m_sed))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6464 -0.4315 0.0184 0.4831 1.6250
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.305e+01 2.335e+00 -5.589 1.78e-07 ***
## SWV 4.950e-03 3.376e-04 14.662 < 2e-16 ***
## density 6.415e-03 1.885e-03 3.404 0.00094 ***
## m_a2 6.667e-03 2.754e-03 2.421 0.01718 *
## hw.vfrac -3.056e+00 1.266e+00 -2.413 0.01755 *
## m_led 2.925e-03 1.113e-03 2.628 0.00987 **
## m_whorliness -6.037e-01 2.500e-01 -2.414 0.01748 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7714 on 106 degrees of freedom
## Multiple R-squared: 0.751, Adjusted R-squared: 0.7369
## F-statistic: 53.27 on 6 and 106 DF, p-value: < 2.2e-16summary(m.best.interactions <- step(lm(E.avg.dyn ~ SWV, L, subset=!is.na(L$m_sed)), direction="forward", scope=E.avg.dyn ~ (SWV+m_volume+m_led+m_sed+m_a0+m_a1+m_a2+m_taper+m_waist+m_ovality+m_whorliness+m_sweep1+m_sweep2+weight+density+hw.vfrac)^2))## Start: AIC=32.16
## E.avg.dyn ~ SWV
##
## Df Sum of Sq RSS AIC
## + density 1 63.066 81.910 -30.359
## + hw.vfrac 1 54.965 90.011 -19.703
## + weight 1 22.231 122.745 15.348
## + m_led 1 15.832 129.144 21.090
## + m_volume 1 14.727 130.249 22.053
## + m_a0 1 14.526 130.450 22.227
## + m_sed 1 14.378 130.598 22.356
## + m_a2 1 12.149 132.828 24.268
## + m_waist 1 12.043 132.933 24.357
## + m_a1 1 9.069 135.907 26.858
## + m_whorliness 1 8.582 136.395 27.263
## + m_sweep2 1 3.763 141.213 31.186
## <none> 144.976 32.158
## + m_taper 1 1.836 143.140 32.718
## + m_ovality 1 0.545 144.431 33.732
## + m_sweep1 1 0.276 144.700 33.942
##
## Step: AIC=-30.36
## E.avg.dyn ~ SWV + density
##
## Df Sum of Sq RSS AIC
## + m_a2 1 7.2599 74.650 -38.847
## + m_waist 1 7.2479 74.662 -38.828
## + m_led 1 7.1454 74.765 -38.673
## + weight 1 6.7086 75.202 -38.015
## + m_volume 1 6.6873 75.223 -37.983
## + m_a0 1 6.6070 75.303 -37.863
## + m_sed 1 6.3559 75.554 -37.486
## + m_a1 1 6.0087 75.902 -36.968
## + m_whorliness 1 3.6326 78.278 -33.485
## + hw.vfrac 1 3.1040 78.806 -32.725
## <none> 81.910 -30.359
## + m_taper 1 0.6047 81.306 -29.197
## + m_ovality 1 0.4056 81.505 -28.920
## + SWV:density 1 0.1833 81.727 -28.612
## + m_sweep2 1 0.1478 81.763 -28.563
## + m_sweep1 1 0.0121 81.898 -28.376
##
## Step: AIC=-38.85
## E.avg.dyn ~ SWV + density + m_a2
##
## Df Sum of Sq RSS AIC
## + hw.vfrac 1 5.4624 69.188 -45.433
## + weight 1 3.5296 71.121 -42.320
## + m_volume 1 3.5154 71.135 -42.297
## + m_sed 1 3.1615 71.489 -41.737
## + m_a0 1 3.1573 71.493 -41.730
## + m_led 1 3.1354 71.515 -41.695
## + m_whorliness 1 3.1338 71.517 -41.693
## + SWV:m_a2 1 2.6754 71.975 -40.971
## + m_a2:density 1 2.2401 72.410 -40.290
## <none> 74.650 -38.847
## + m_ovality 1 0.6438 74.007 -37.825
## + m_sweep2 1 0.1350 74.515 -37.051
## + SWV:density 1 0.1239 74.526 -37.034
## + m_sweep1 1 0.1048 74.546 -37.005
## + m_a1 1 0.0044 74.646 -36.853
## + m_taper 1 0.0043 74.646 -36.853
## + m_waist 1 0.0035 74.647 -36.852
##
## Step: AIC=-45.43
## E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac
##
## Df Sum of Sq RSS AIC
## + m_led 1 2.64503 66.543 -47.838
## + m_volume 1 2.59123 66.597 -47.747
## + weight 1 2.52886 66.659 -47.641
## + m_sed 1 2.51511 66.673 -47.618
## + m_a0 1 2.46386 66.724 -47.531
## + SWV:m_a2 1 2.01216 67.176 -46.768
## + m_whorliness 1 2.00495 67.183 -46.756
## + density:hw.vfrac 1 1.25421 67.934 -45.501
## + m_a2:density 1 1.23779 67.950 -45.473
## <none> 69.188 -45.433
## + m_a2:hw.vfrac 1 1.04102 68.147 -45.146
## + m_ovality 1 1.02793 68.160 -45.125
## + m_sweep2 1 0.36878 68.819 -44.037
## + m_waist 1 0.36309 68.825 -44.028
## + SWV:density 1 0.24205 68.946 -43.829
## + SWV:hw.vfrac 1 0.16756 69.020 -43.707
## + m_a1 1 0.00079 69.187 -43.435
## + m_sweep1 1 0.00056 69.187 -43.434
## + m_taper 1 0.00028 69.188 -43.434
##
## Step: AIC=-47.84
## E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led
##
## Df Sum of Sq RSS AIC
## + m_whorliness 1 3.4686 63.074 -51.887
## + m_led:m_a2 1 2.6380 63.905 -50.409
## + SWV:m_a2 1 2.2078 64.335 -49.651
## + m_a2:density 1 1.2584 65.285 -47.995
## <none> 66.543 -47.838
## + m_a2:hw.vfrac 1 1.0760 65.467 -47.680
## + density:hw.vfrac 1 0.9964 65.547 -47.543
## + m_ovality 1 0.6808 65.862 -47.000
## + m_waist 1 0.5291 66.014 -46.740
## + m_sweep2 1 0.3435 66.200 -46.423
## + m_taper 1 0.2110 66.332 -46.197
## + SWV:density 1 0.2044 66.339 -46.186
## + m_a1 1 0.2024 66.341 -46.182
## + SWV:hw.vfrac 1 0.1393 66.404 -46.075
## + m_led:hw.vfrac 1 0.0976 66.445 -46.004
## + m_led:density 1 0.0155 66.527 -45.864
## + SWV:m_led 1 0.0131 66.530 -45.860
## + m_a0 1 0.0110 66.532 -45.857
## + m_volume 1 0.0059 66.537 -45.848
## + m_sweep1 1 0.0008 66.542 -45.839
## + m_sed 1 0.0002 66.543 -45.838
## + weight 1 0.0001 66.543 -45.838
##
## Step: AIC=-51.89
## E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness
##
## Df Sum of Sq RSS AIC
## + m_led:m_a2 1 1.62153 61.453 -52.830
## + density:hw.vfrac 1 1.32539 61.749 -52.287
## <none> 63.074 -51.887
## + m_whorliness:density 1 1.10587 61.968 -51.886
## + SWV:m_a2 1 1.09501 61.979 -51.866
## + m_a2:density 1 0.74870 62.326 -51.237
## + SWV:m_whorliness 1 0.70673 62.368 -51.161
## + m_led:hw.vfrac 1 0.47861 62.596 -50.748
## + m_whorliness:hw.vfrac 1 0.46991 62.604 -50.732
## + m_waist 1 0.46288 62.611 -50.720
## + m_led:m_whorliness 1 0.43648 62.638 -50.672
## + m_a2:hw.vfrac 1 0.39091 62.683 -50.590
## + SWV:density 1 0.34270 62.732 -50.503
## + m_led:density 1 0.33318 62.741 -50.486
## + m_sed 1 0.31475 62.760 -50.453
## + weight 1 0.29155 62.783 -50.411
## + m_ovality 1 0.28823 62.786 -50.405
## + m_a0 1 0.26551 62.809 -50.364
## + SWV:hw.vfrac 1 0.13529 62.939 -50.130
## + m_volume 1 0.11511 62.959 -50.094
## + m_sweep1 1 0.06520 63.009 -50.004
## + SWV:m_led 1 0.03334 63.041 -49.947
## + m_a2:m_whorliness 1 0.01886 63.055 -49.921
## + m_sweep2 1 0.00538 63.069 -49.897
## + m_taper 1 0.00252 63.072 -49.892
## + m_a1 1 0.00116 63.073 -49.889
##
## Step: AIC=-52.83
## E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness +
## m_a2:m_led
##
## Df Sum of Sq RSS AIC
## + m_whorliness:density 1 1.18572 60.267 -53.032
## <none> 61.453 -52.830
## + SWV:m_whorliness 1 0.98449 60.468 -52.655
## + density:hw.vfrac 1 0.94377 60.509 -52.579
## + m_ovality 1 0.46033 60.993 -51.680
## + m_led:hw.vfrac 1 0.44254 61.010 -51.647
## + SWV:m_a2 1 0.42581 61.027 -51.616
## + m_led:density 1 0.41275 61.040 -51.592
## + m_waist 1 0.40028 61.053 -51.569
## + m_whorliness:hw.vfrac 1 0.37766 61.075 -51.527
## + weight 1 0.36951 61.083 -51.512
## + SWV:density 1 0.35161 61.101 -51.479
## + m_a2:density 1 0.32596 61.127 -51.431
## + m_volume 1 0.17885 61.274 -51.160
## + m_sed 1 0.16182 61.291 -51.128
## + m_led:m_whorliness 1 0.14199 61.311 -51.092
## + SWV:hw.vfrac 1 0.09910 61.354 -51.013
## + m_a0 1 0.07901 61.374 -50.976
## + m_taper 1 0.07458 61.378 -50.968
## + m_a1 1 0.06753 61.385 -50.955
## + m_a2:hw.vfrac 1 0.06065 61.392 -50.942
## + m_sweep1 1 0.05127 61.402 -50.925
## + m_a2:m_whorliness 1 0.03599 61.417 -50.897
## + m_sweep2 1 0.00053 61.452 -50.831
## + SWV:m_led 1 0.00017 61.453 -50.831
##
## Step: AIC=-53.03
## E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness +
## m_a2:m_led + density:m_whorliness
##
## Df Sum of Sq RSS AIC
## + SWV:m_whorliness 1 1.99936 58.268 -54.844
## <none> 60.267 -53.032
## + density:hw.vfrac 1 0.92779 59.339 -52.785
## + m_ovality 1 0.60108 59.666 -52.165
## + m_waist 1 0.52976 59.737 -52.030
## + m_a2:density 1 0.38661 59.880 -51.759
## + weight 1 0.36104 59.906 -51.711
## + m_led:hw.vfrac 1 0.33776 59.929 -51.667
## + SWV:m_a2 1 0.27892 59.988 -51.556
## + m_led:density 1 0.21277 60.054 -51.432
## + m_a2:m_whorliness 1 0.18906 60.078 -51.387
## + m_volume 1 0.17331 60.094 -51.357
## + SWV:density 1 0.12362 60.143 -51.264
## + m_sed 1 0.10532 60.162 -51.230
## + m_a2:hw.vfrac 1 0.08905 60.178 -51.199
## + m_whorliness:hw.vfrac 1 0.07444 60.193 -51.172
## + m_a0 1 0.06888 60.198 -51.161
## + m_taper 1 0.05986 60.207 -51.144
## + m_a1 1 0.05237 60.215 -51.130
## + m_sweep1 1 0.02050 60.247 -51.070
## + m_led:m_whorliness 1 0.01753 60.250 -51.065
## + SWV:hw.vfrac 1 0.01396 60.253 -51.058
## + SWV:m_led 1 0.00093 60.266 -51.034
## + m_sweep2 1 0.00000 60.267 -51.032
##
## Step: AIC=-54.84
## E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness +
## m_a2:m_led + density:m_whorliness + SWV:m_whorliness
##
## Df Sum of Sq RSS AIC
## + m_led:m_whorliness 1 1.04859 57.219 -54.896
## <none> 58.268 -54.844
## + density:hw.vfrac 1 0.95255 57.315 -54.707
## + weight 1 0.78312 57.485 -54.373
## + m_waist 1 0.64941 57.618 -54.111
## + m_volume 1 0.58231 57.685 -53.979
## + m_led:hw.vfrac 1 0.43804 57.830 -53.697
## + m_ovality 1 0.41814 57.850 -53.658
## + m_a2:density 1 0.31931 57.948 -53.465
## + SWV:m_a2 1 0.27028 57.997 -53.370
## + m_led:density 1 0.20619 58.062 -53.245
## + m_a0 1 0.19906 58.069 -53.231
## + m_sed 1 0.19763 58.070 -53.228
## + SWV:m_led 1 0.11646 58.151 -53.070
## + m_whorliness:hw.vfrac 1 0.07849 58.189 -52.997
## + m_sweep1 1 0.06359 58.204 -52.968
## + m_a2:hw.vfrac 1 0.06256 58.205 -52.966
## + m_sweep2 1 0.05494 58.213 -52.951
## + SWV:density 1 0.03690 58.231 -52.916
## + m_a2:m_whorliness 1 0.00567 58.262 -52.855
## + m_taper 1 0.00165 58.266 -52.848
## + m_a1 1 0.00054 58.267 -52.845
## + SWV:hw.vfrac 1 0.00001 58.268 -52.844
##
## Step: AIC=-54.9
## E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness +
## m_a2:m_led + density:m_whorliness + SWV:m_whorliness + m_led:m_whorliness
##
## Df Sum of Sq RSS AIC
## + density:hw.vfrac 1 1.26034 55.959 -55.413
## <none> 57.219 -54.896
## + m_led:hw.vfrac 1 0.63997 56.579 -54.167
## + weight 1 0.63310 56.586 -54.154
## + m_led:density 1 0.54586 56.673 -53.980
## + m_waist 1 0.53859 56.681 -53.965
## + m_ovality 1 0.44704 56.772 -53.783
## + m_a2:density 1 0.33990 56.879 -53.570
## + m_volume 1 0.33884 56.880 -53.568
## + SWV:m_a2 1 0.25253 56.967 -53.396
## + m_sed 1 0.15448 57.065 -53.202
## + m_a0 1 0.14583 57.073 -53.185
## + SWV:m_led 1 0.11630 57.103 -53.126
## + SWV:density 1 0.07720 57.142 -53.049
## + m_whorliness:hw.vfrac 1 0.04516 57.174 -52.986
## + m_sweep1 1 0.03898 57.180 -52.973
## + m_taper 1 0.03669 57.182 -52.969
## + m_a1 1 0.02901 57.190 -52.954
## + m_a2:hw.vfrac 1 0.02896 57.190 -52.954
## + m_sweep2 1 0.02015 57.199 -52.936
## + m_a2:m_whorliness 1 0.01546 57.204 -52.927
## + SWV:hw.vfrac 1 0.00293 57.216 -52.902
##
## Step: AIC=-55.41
## E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness +
## m_a2:m_led + density:m_whorliness + SWV:m_whorliness + m_led:m_whorliness +
## density:hw.vfrac
##
## Df Sum of Sq RSS AIC
## <none> 55.959 -55.413
## + m_waist 1 0.43455 55.524 -54.294
## + SWV:m_a2 1 0.36937 55.589 -54.162
## + m_a2:density 1 0.33606 55.623 -54.094
## + weight 1 0.31425 55.645 -54.050
## + m_led:density 1 0.30622 55.653 -54.033
## + m_led:hw.vfrac 1 0.27090 55.688 -53.962
## + m_ovality 1 0.19033 55.768 -53.798
## + m_volume 1 0.14025 55.819 -53.697
## + SWV:m_led 1 0.10454 55.854 -53.624
## + m_taper 1 0.08987 55.869 -53.595
## + m_a2:hw.vfrac 1 0.08267 55.876 -53.580
## + m_a1 1 0.07854 55.880 -53.572
## + m_a0 1 0.05517 55.904 -53.525
## + m_sed 1 0.04600 55.913 -53.506
## + m_sweep1 1 0.04150 55.917 -53.497
## + SWV:hw.vfrac 1 0.03258 55.926 -53.479
## + m_whorliness:hw.vfrac 1 0.01377 55.945 -53.441
## + m_sweep2 1 0.00264 55.956 -53.419
## + SWV:density 1 0.00097 55.958 -53.415
## + m_a2:m_whorliness 1 0.00001 55.959 -53.413##
## Call:
## lm(formula = E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led +
## m_whorliness + m_a2:m_led + density:m_whorliness + SWV:m_whorliness +
## m_led:m_whorliness + density:hw.vfrac, data = L, subset = !is.na(L$m_sed))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.15571 -0.48704 0.05985 0.42004 1.60248
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.855e+01 7.102e+00 -4.021 0.000112 ***
## SWV 7.578e-03 1.131e-03 6.700 1.21e-09 ***
## density 1.221e-02 5.266e-03 2.318 0.022485 *
## m_a2 -1.040e-02 1.277e-02 -0.815 0.417175
## hw.vfrac -1.240e+01 6.253e+00 -1.982 0.050163 .
## m_led 6.837e-03 3.208e-03 2.131 0.035504 *
## m_whorliness 2.072e+01 7.598e+00 2.727 0.007532 **
## m_a2:m_led 4.394e-05 3.206e-05 1.371 0.173512
## density:m_whorliness -1.053e-02 5.598e-03 -1.882 0.062777 .
## SWV:m_whorliness -2.926e-03 1.195e-03 -2.449 0.016039 *
## m_led:m_whorliness -5.302e-03 3.388e-03 -1.565 0.120793
## density:hw.vfrac 1.038e-02 6.885e-03 1.508 0.134615
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7443 on 101 degrees of freedom
## Multiple R-squared: 0.7791, Adjusted R-squared: 0.755
## F-statistic: 32.38 on 11 and 101 DF, p-value: < 2.2e-16#
m.best.current <- step(lm(E.avg.dyn ~ SWV, L, subset=!is.na(L$m_sed)), direction="forward", scope=E.avg.dyn ~ (m_volume+volume+SWV))## Start: AIC=32.16
## E.avg.dyn ~ SWV
##
## Df Sum of Sq RSS AIC
## + volume 1 14.732 130.24 22.049
## + m_volume 1 14.727 130.25 22.053
## <none> 144.98 32.158
##
## Step: AIC=22.05
## E.avg.dyn ~ SWV + volume
##
## Df Sum of Sq RSS AIC
## <none> 130.24 22.049
## + m_volume 1 0.0013448 130.24 24.048m.best.withWeight <- step(lm(E.avg.dyn ~ SWV, L, subset=!is.na(L$m_sed)), direction="forward", scope=E.avg.dyn ~ (m_volume+volume+SWV+weight+density+hw.vfrac))## Start: AIC=32.16
## E.avg.dyn ~ SWV
##
## Df Sum of Sq RSS AIC
## + density 1 63.066 81.910 -30.359
## + hw.vfrac 1 54.965 90.011 -19.703
## + weight 1 22.231 122.745 15.348
## + volume 1 14.732 130.244 22.049
## + m_volume 1 14.727 130.249 22.053
## <none> 144.976 32.158
##
## Step: AIC=-30.36
## E.avg.dyn ~ SWV + density
##
## Df Sum of Sq RSS AIC
## + weight 1 6.7086 75.202 -38.015
## + volume 1 6.6960 75.214 -37.996
## + m_volume 1 6.6873 75.223 -37.983
## + hw.vfrac 1 3.1040 78.806 -32.725
## <none> 81.910 -30.359
##
## Step: AIC=-38.02
## E.avg.dyn ~ SWV + density + weight
##
## Df Sum of Sq RSS AIC
## + hw.vfrac 1 2.56610 72.636 -39.938
## <none> 75.202 -38.015
## + m_volume 1 0.00010 75.202 -36.015
## + volume 1 0.00007 75.202 -36.015
##
## Step: AIC=-39.94
## E.avg.dyn ~ SWV + density + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## <none> 72.636 -39.938
## + m_volume 1 0.077844 72.558 -38.060
## + volume 1 0.035007 72.601 -37.993# currently measurable: SWV, volume/size, shape
# with weight: SWV, volume/size, shape, weight, density
m.best.withWeight <- step(lm(E.avg.dyn ~ SWV, L, subset=!is.na(L$m_sed)), direction="forward", scope=E.avg.dyn ~ (m_volume+volume+SWV+weight+density+hw.vfrac))## Start: AIC=32.16
## E.avg.dyn ~ SWV
##
## Df Sum of Sq RSS AIC
## + density 1 63.066 81.910 -30.359
## + hw.vfrac 1 54.965 90.011 -19.703
## + weight 1 22.231 122.745 15.348
## + volume 1 14.732 130.244 22.049
## + m_volume 1 14.727 130.249 22.053
## <none> 144.976 32.158
##
## Step: AIC=-30.36
## E.avg.dyn ~ SWV + density
##
## Df Sum of Sq RSS AIC
## + weight 1 6.7086 75.202 -38.015
## + volume 1 6.6960 75.214 -37.996
## + m_volume 1 6.6873 75.223 -37.983
## + hw.vfrac 1 3.1040 78.806 -32.725
## <none> 81.910 -30.359
##
## Step: AIC=-38.02
## E.avg.dyn ~ SWV + density + weight
##
## Df Sum of Sq RSS AIC
## + hw.vfrac 1 2.56610 72.636 -39.938
## <none> 75.202 -38.015
## + m_volume 1 0.00010 75.202 -36.015
## + volume 1 0.00007 75.202 -36.015
##
## Step: AIC=-39.94
## E.avg.dyn ~ SWV + density + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## <none> 72.636 -39.938
## + m_volume 1 0.077844 72.558 -38.060
## + volume 1 0.035007 72.601 -37.993m.best.withHW <- step(lm(E.avg.dyn ~ SWV, L, subset=!is.na(L$m_sed)), direction="forward", scope=E.avg.dyn ~ (m_volume+volume+SWV+hw.vfrac))## Start: AIC=32.16
## E.avg.dyn ~ SWV
##
## Df Sum of Sq RSS AIC
## + hw.vfrac 1 54.965 90.011 -19.703
## + volume 1 14.732 130.244 22.049
## + m_volume 1 14.727 130.249 22.053
## <none> 144.976 32.158
##
## Step: AIC=-19.7
## E.avg.dyn ~ SWV + hw.vfrac
##
## Df Sum of Sq RSS AIC
## + m_volume 1 7.1805 82.830 -27.097
## + volume 1 7.0800 82.931 -26.960
## <none> 90.011 -19.703
##
## Step: AIC=-27.1
## E.avg.dyn ~ SWV + hw.vfrac + m_volume
##
## Df Sum of Sq RSS AIC
## <none> 82.830 -27.097
## + volume 1 0.33081 82.499 -25.549# with heartwood: SWV, volume/size, shape, weight, density, hw.vfrac
m.best.withWeightHW <- step(lm(E.avg.dyn ~ (SWV + density + weight + hw.vfrac), L))## Start: AIC=-32.68
## E.avg.dyn ~ (SWV + density + weight + hw.vfrac)
##
## Df Sum of Sq RSS AIC
## <none> 86.940 -32.676
## - hw.vfrac 1 4.864 91.804 -27.981
## - weight 1 6.082 93.023 -26.359
## - density 1 9.077 96.017 -22.462
## - SWV 1 177.134 264.075 101.978Plot best models for Marco:
myplot=function (m, lbl) {
print(s<-summary(m))
xyplot(m$model$E.avg.dyn ~ predict(m),
panel=function(x,y,...) {
panel.abline(c(0,1), col='grey70', lty=2)
panel.xyplot(x,y,...)},
aspect='iso',
xlab='Predicted', ylab='Actual',
main=sprintf('%s\nr^2=%0.2f',lbl,s$r.squared))
#main=lbl, sub=expression(r^2==s$r.squared))
}
library(gridExtra)
grid.arrange(myplot(m.best.current, lbl="Current (SWV + volume)"),
myplot(m.best.withWeight, lbl="With Log Weight"),
myplot(m.best.withHW, lbl="With Log end HW"),
myplot(m.best, lbl="Everything"),
nrow=1,
as.table=TRUE)
##
## Call:
## lm(formula = E.avg.dyn ~ SWV + volume, data = L, subset = !is.na(L$m_sed))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2052 -0.5780 -0.0167 0.6974 2.6003
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.6134051 1.5535782 -4.257 4.38e-05 ***
## SWV 0.0045176 0.0004435 10.186 < 2e-16 ***
## volume 1.8940993 0.5369809 3.527 0.000614 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.088 on 110 degrees of freedom
## Multiple R-squared: 0.4858, Adjusted R-squared: 0.4764
## F-statistic: 51.95 on 2 and 110 DF, p-value: < 2.2e-16
##
##
## Call:
## lm(formula = E.avg.dyn ~ SWV + density + weight + hw.vfrac, data = L,
## subset = !is.na(L$m_sed))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8825 -0.3698 0.0792 0.5017 1.8583
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.491e+01 2.321e+00 -6.425 3.65e-09 ***
## SWV 5.256e-03 3.471e-04 15.144 < 2e-16 ***
## density 7.229e-03 1.968e-03 3.674 0.000373 ***
## weight 1.296e-03 4.280e-04 3.029 0.003069 **
## hw.vfrac -2.537e+00 1.299e+00 -1.953 0.053368 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8201 on 108 degrees of freedom
## Multiple R-squared: 0.7132, Adjusted R-squared: 0.7026
## F-statistic: 67.15 on 4 and 108 DF, p-value: < 2.2e-16
##
##
## Call:
## lm(formula = E.avg.dyn ~ SWV + hw.vfrac + m_volume, data = L,
## subset = !is.na(L$m_sed))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8128 -0.4506 0.0068 0.5024 1.8673
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.3996536 1.2464693 -5.936 3.52e-08 ***
## SWV 0.0053022 0.0003687 14.381 < 2e-16 ***
## hw.vfrac -6.6073978 0.8364394 -7.899 2.40e-12 ***
## m_volume 1.4196155 0.4618222 3.074 0.00267 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8717 on 109 degrees of freedom
## Multiple R-squared: 0.673, Adjusted R-squared: 0.664
## F-statistic: 74.76 on 3 and 109 DF, p-value: < 2.2e-16
##
##
## Call:
## lm(formula = E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led +
## m_whorliness, data = L, subset = !is.na(L$m_sed))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6464 -0.4315 0.0184 0.4831 1.6250
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.305e+01 2.335e+00 -5.589 1.78e-07 ***
## SWV 4.950e-03 3.376e-04 14.662 < 2e-16 ***
## density 6.415e-03 1.885e-03 3.404 0.00094 ***
## m_a2 6.667e-03 2.754e-03 2.421 0.01718 *
## hw.vfrac -3.056e+00 1.266e+00 -2.413 0.01755 *
## m_led 2.925e-03 1.113e-03 2.628 0.00987 **
## m_whorliness -6.037e-01 2.500e-01 -2.414 0.01748 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7714 on 106 degrees of freedom
## Multiple R-squared: 0.751, Adjusted R-squared: 0.7369
## F-statistic: 53.27 on 6 and 106 DF, p-value: < 2.2e-16Does eliminating logs with significant compression wood visible on ends lead to better models?
what % of lumber that is not from the cant has stiffness below 6GPa?
2.2 Warp
2.2.1 Best Linear Models
Use stepwise regression to select ‘best’ (in terms of AIC) linear model.
best.linear.model = function (y,LL=NULL,plot=TRUE,...) {
if (is.null(LL)) {
LL=L[,predictors]
}
LL$y = y
LL = na.omit(LL)#[complete.cases(LL),]
scope = formula(paste("y~",paste(predictors,collapse='+')))
#print(scope)
m.init <- lm(y ~ SWV, LL, ...)
#browser()
m <- step(m.init, direction="both", scope=scope, trace=0)
if (plot) {
print(xyplot(LL$y ~ predict(m),aspect='iso',
panel=function(...){
panel.abline(c(0,1),col='grey70')
panel.xyplot(...)},
main=deparse(substitute(y)),
xlab='predicted', ylab='observed'))
}
return(m)
}2.2.1.1 Bow
summary(m.best.bow_avg <- best.linear.model(L$bow_avg))
##
## Call:
## lm(formula = y ~ SWV + density + m_whorliness + m_taper, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.5526 -2.1948 -0.5343 1.6360 15.9240
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 50.677145 8.169233 6.203 1.04e-08 ***
## SWV -0.006277 0.001527 -4.112 7.67e-05 ***
## density -0.016368 0.005161 -3.172 0.00197 **
## m_whorliness -2.400092 1.140879 -2.104 0.03772 *
## m_taper -0.233965 0.125925 -1.858 0.06590 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.566 on 108 degrees of freedom
## Multiple R-squared: 0.1962, Adjusted R-squared: 0.1664
## F-statistic: 6.589 on 4 and 108 DF, p-value: 8.771e-05summary(m.best.bow_p50 <- best.linear.model(L$bow_p50))
##
## Call:
## lm(formula = y ~ SWV + weight + m_whorliness + density + m_taper,
## data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.0144 -2.2305 -0.3083 2.2684 10.3289
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 53.284684 8.380541 6.358 5.13e-09 ***
## SWV -0.006804 0.001591 -4.277 4.13e-05 ***
## weight -0.004386 0.001964 -2.233 0.02765 *
## m_whorliness -3.358372 1.173856 -2.861 0.00508 **
## density -0.016345 0.005515 -2.964 0.00375 **
## m_taper -0.220894 0.132053 -1.673 0.09729 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.658 on 107 degrees of freedom
## Multiple R-squared: 0.2698, Adjusted R-squared: 0.2357
## F-statistic: 7.908 on 5 and 107 DF, p-value: 2.269e-06summary(m.best.bow_p75 <- best.linear.model(L$bow_p75))
##
## Call:
## lm(formula = y ~ SWV + m_taper + density + m_whorliness, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.7632 -3.5605 -0.7889 2.4524 19.9506
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 62.694698 11.674862 5.370 4.55e-07 ***
## SWV -0.007537 0.002182 -3.455 0.000788 ***
## m_taper -0.435954 0.179963 -2.422 0.017080 *
## density -0.018750 0.007375 -2.542 0.012430 *
## m_whorliness -2.673998 1.630459 -1.640 0.103909
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.097 on 108 degrees of freedom
## Multiple R-squared: 0.1633, Adjusted R-squared: 0.1323
## F-statistic: 5.271 on 4 and 108 DF, p-value: 0.0006458SWV, weight, density, whorliness and taper best predictors. Median bow best predicted.
2.2.1.2 Crook
summary(m.best.crook_avg <- best.linear.model(L$crook_avg))
##
## Call:
## lm(formula = y ~ density + sweep.prod + m_waist + m_a2, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2187 -1.4615 -0.2139 1.0972 10.3181
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.094252 2.899470 4.171 6.14e-05 ***
## density -0.008411 0.003038 -2.768 0.00663 **
## sweep.prod 2.042667 0.857806 2.381 0.01900 *
## m_waist -38.089688 25.865324 -1.473 0.14376
## m_a2 0.404822 0.283115 1.430 0.15564
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.165 on 108 degrees of freedom
## Multiple R-squared: 0.1638, Adjusted R-squared: 0.1328
## F-statistic: 5.288 on 4 and 108 DF, p-value: 0.0006287summary(m.best.crook_p50 <- best.linear.model(L$crook_p50))
##
## Call:
## lm(formula = y ~ density + sweep.prod + m_waist + m_a2, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4437 -1.4283 -0.2151 0.9080 8.1531
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.402026 2.636957 4.324 3.43e-05 ***
## density -0.008649 0.002763 -3.130 0.00225 **
## sweep.prod 2.012259 0.780142 2.579 0.01124 *
## m_waist -42.110793 23.523518 -1.790 0.07623 .
## m_a2 0.451084 0.257482 1.752 0.08263 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.969 on 108 degrees of freedom
## Multiple R-squared: 0.1886, Adjusted R-squared: 0.1586
## F-statistic: 6.278 on 4 and 108 DF, p-value: 0.00014summary(m.best.crook_p75 <- best.linear.model(L$crook_p75))
##
## Call:
## lm(formula = y ~ density + sweep.prod, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2717 -1.7721 -0.6979 1.4346 14.7374
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.331465 3.918569 4.678 8.29e-06 ***
## density -0.013853 0.004101 -3.378 0.00101 **
## sweep.prod 2.993362 1.161504 2.577 0.01129 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.935 on 110 degrees of freedom
## Multiple R-squared: 0.1628, Adjusted R-squared: 0.1476
## F-statistic: 10.7 on 2 and 110 DF, p-value: 5.689e-0575%ile crook best predicted. Density, sweep.prod, SWV, whorliness.
2.2.1.3 Combination Warp
Stan combined bow and crook using WPA grade limits.
combined_warp = L$crook_avg/75 + L$bow_avg/25
summary(m.best.combined_warp <- best.linear.model(combined_warp))
##
## Call:
## lm(formula = y ~ SWV + density + m_whorliness + m_taper, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.34202 -0.09823 -0.01804 0.06120 0.71273
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.325e+00 3.445e-01 6.749 7.71e-10 ***
## SWV -2.772e-04 6.437e-05 -4.307 3.66e-05 ***
## density -8.028e-04 2.176e-04 -3.689 0.000354 ***
## m_whorliness -9.801e-02 4.811e-02 -2.037 0.044078 *
## m_taper -1.017e-02 5.310e-03 -1.915 0.058111 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1504 on 108 degrees of freedom
## Multiple R-squared: 0.2187, Adjusted R-squared: 0.1898
## F-statistic: 7.559 on 4 and 108 DF, p-value: 2.085e-05What happens if we toss a couple of outliers?
L$SWILogNumber[combined_warp>1.2] # 222## [1] 222L$SWILogNumber[combined_warp<0.2] # 103## [1] 103subset = !L$SWILogNumber%in%c(103,222)
summary(m.best.combined_warp <- best.linear.model(combined_warp[subset], LL=L[subset,predictors]))
##
## Call:
## lm(formula = y ~ SWV + m_taper + density + m_whorliness, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.23075 -0.08899 -0.01509 0.06012 0.40411
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.115e+00 3.004e-01 7.042 1.97e-10 ***
## SWV -2.472e-04 5.615e-05 -4.403 2.56e-05 ***
## m_taper -1.272e-02 4.620e-03 -2.752 0.006963 **
## density -6.745e-04 1.904e-04 -3.542 0.000592 ***
## m_whorliness -8.497e-02 4.193e-02 -2.026 0.045228 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1303 on 106 degrees of freedom
## Multiple R-squared: 0.2428, Adjusted R-squared: 0.2142
## F-statistic: 8.497 on 4 and 106 DF, p-value: 5.499e-06A little better, but nothing spectacular.
Can a random forest model do better?
library(randomForest)## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.LL=L[,predictors]
LL$combined_warp = combined_warp
LL = na.omit(LL)
m.rf <- randomForest(combined_warp ~ ., LL, importance=TRUE, ntree=50000)
par(mfcol=c(1,1))
varImpPlot(m.rf)
print(m.rf)##
## Call:
## randomForest(formula = combined_warp ~ ., data = LL, importance = TRUE, ntree = 50000)
## Type of random forest: regression
## Number of trees: 50000
## No. of variables tried at each split: 5
##
## Mean of squared residuals: 0.02799233
## % Var explained: -1.18xyplot(LL$combined_warp ~ predict(m.rf), aspect='iso')
Lousy results, but interesting that log green density turns up as the most favoured predictor, followed by SWV, taper and ovality.
What about other variable selection approaches?
library(subselect)
LL <- L[,predictors]
for (lmeas in c('E.avg.dyn','crook_avg','crook_avg_inner','crook_avg_outer','bow_avg','twist_avg')) {
LL$y = L[,lmeas]
m.0 <- lm(y ~ ., LL)
Hmat <- lmHmat(m.0)
Eleaps <- eleaps(Hmat$mat, kmin=1, kmax=length(predictors)-1, H=Hmat$H, r=Hmat$r)
plot(Eleaps$bestvalues, type='b', main=lmeas, ylab="model goodness", xlab="number of predictors")
cat(paste("\n",lmeas,"\n"))
for (i in 1:nrow(Eleaps$bestsets)) {
cat(paste(i,': ',paste(colnames(Hmat$mat)[Eleaps$bestsets[i,1:i]],collapse=", "),"\n", sep=""))
# AIC?
}
}
##
## E.avg.dyn
## 1: SWV
## 2: SWV, density
## 3: SWV, m_a2, density
## 4: SWV, m_led, m_whorliness, density
## 5: SWV, m_led, m_a2, m_whorliness, density
## 6: SWV, m_led, m_a2, m_whorliness, density, hw.vfrac
## 7: SWV, m_led, m_a0, m_a1, m_whorliness, density, hw.vfrac
## 8: SWV, m_led, m_a0, m_a1, m_waist, m_whorliness, density, hw.vfrac
## 9: SWV, m_volume, m_led, m_a0, m_a1, m_waist, m_whorliness, density, hw.vfrac
## 10: SWV, m_volume, m_led, m_a0, m_a1, m_waist, m_whorliness, weight, density, hw.vfrac
## 11: SWV, m_volume, m_led, m_a0, m_a1, m_waist, m_ovality, m_whorliness, weight, density, hw.vfrac
## 12: SWV, m_volume, m_led, m_a0, m_a1, m_a2, m_waist, m_ovality, m_whorliness, weight, density, hw.vfrac
## 13: SWV, m_volume, m_led, m_a0, m_a1, m_a2, m_waist, m_ovality, m_whorliness, m_sweep1, weight, density, hw.vfrac
## 14: SWV, m_volume, m_led, m_a0, m_a1, m_a2, m_waist, m_ovality, m_whorliness, m_sweep1, weight, density, hw.vfrac, sweep.prod
## 15: SWV, m_volume, m_led, m_a0, m_a1, m_a2, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, weight, density, hw.vfrac, sweep.prod
## 16: SWV, m_volume, m_led, m_sed, m_a0, m_a1, m_a2, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, weight, density, hw.vfrac, sweep.prod
##
## crook_avg
## 1: density
## 2: density, sweep.prod
## 3: m_waist, density, sweep.prod
## 4: m_a1, m_taper, m_waist, density
## 5: m_a1, m_taper, m_waist, density, sweep.prod
## 6: SWV, m_a1, m_taper, m_waist, density, sweep.prod
## 7: m_volume, m_a1, m_taper, m_waist, weight, density, sweep.prod
## 8: m_volume, m_sed, m_a1, m_taper, m_waist, weight, density, sweep.prod
## 9: SWV, m_volume, m_sed, m_a1, m_taper, m_waist, weight, density, sweep.prod
## 10: SWV, m_volume, m_sed, m_a1, m_taper, m_waist, m_ovality, weight, density, sweep.prod
## 11: SWV, m_volume, m_sed, m_a1, m_taper, m_waist, m_ovality, weight, density, hw.vfrac, sweep.prod
## 12: SWV, m_volume, m_sed, m_a1, m_taper, m_waist, m_ovality, m_whorliness, weight, density, hw.vfrac, sweep.prod
## 13: SWV, m_volume, m_sed, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, weight, density, hw.vfrac, sweep.prod
## 14: SWV, m_volume, m_led, m_sed, m_a0, m_a1, m_taper, m_waist, m_ovality, m_whorliness, weight, density, hw.vfrac, sweep.prod
## 15: SWV, m_volume, m_led, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, weight, density, hw.vfrac, sweep.prod
## 16: SWV, m_volume, m_led, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, weight, density, hw.vfrac, sweep.prod
##
## crook_avg_inner
## 1: density
## 2: m_sed, density
## 3: m_sed, m_a1, density
## 4: m_led, m_a2, m_waist, density
## 5: m_sed, m_a1, m_taper, m_waist, density
## 6: m_sed, m_a1, m_taper, m_waist, density, sweep.prod
## 7: SWV, m_sed, m_a1, m_taper, m_waist, weight, density
## 8: SWV, m_sed, m_a1, m_taper, m_waist, m_ovality, weight, density
## 9: SWV, m_sed, m_a1, m_taper, m_waist, m_whorliness, weight, density, sweep.prod
## 10: SWV, m_sed, m_a1, m_taper, m_waist, m_ovality, m_whorliness, weight, density, sweep.prod
## 11: SWV, m_sed, m_a1, m_taper, m_waist, m_ovality, m_whorliness, weight, density, hw.vfrac, sweep.prod
## 12: SWV, m_volume, m_sed, m_a1, m_taper, m_waist, m_ovality, m_whorliness, weight, density, hw.vfrac, sweep.prod
## 13: SWV, m_volume, m_sed, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, weight, density, hw.vfrac, sweep.prod
## 14: SWV, m_volume, m_sed, m_a1, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, weight, density, hw.vfrac, sweep.prod
## 15: SWV, m_volume, m_sed, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, weight, density, hw.vfrac, sweep.prod
## 16: SWV, m_volume, m_led, m_sed, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, weight, density, hw.vfrac, sweep.prod
##
## crook_avg_outer
## 1: SWV
## 2: SWV, m_waist
## 3: SWV, m_a2, m_waist
## 4: SWV, m_a1, m_taper, m_waist
## 5: SWV, m_a1, m_taper, m_waist, m_sweep1
## 6: SWV, m_a1, m_taper, m_waist, m_whorliness, m_sweep1
## 7: SWV, m_a1, m_taper, m_waist, m_sweep2, hw.vfrac, sweep.prod
## 8: SWV, m_a1, m_taper, m_waist, m_sweep2, weight, hw.vfrac, sweep.prod
## 9: SWV, m_a1, m_taper, m_waist, m_ovality, m_sweep2, weight, hw.vfrac, sweep.prod
## 10: SWV, m_a1, m_taper, m_waist, m_ovality, m_sweep1, m_sweep2, weight, hw.vfrac, sweep.prod
## 11: SWV, m_sed, m_a0, m_a1, m_taper, m_waist, m_ovality, m_sweep1, m_sweep2, hw.vfrac, sweep.prod
## 12: SWV, m_sed, m_a0, m_a1, m_taper, m_waist, m_ovality, m_whorliness, m_sweep2, weight, hw.vfrac, sweep.prod
## 13: SWV, m_sed, m_a0, m_a1, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, weight, hw.vfrac, sweep.prod
## 14: SWV, m_volume, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, hw.vfrac, sweep.prod
## 15: SWV, m_volume, m_led, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, hw.vfrac, sweep.prod
## 16: SWV, m_led, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, weight, density, hw.vfrac, sweep.prod
##
## bow_avg
## 1: SWV
## 2: SWV, density
## 3: SWV, m_whorliness, density
## 4: SWV, m_taper, m_whorliness, density
## 5: SWV, m_volume, m_a0, m_whorliness, density
## 6: SWV, m_volume, m_a0, m_taper, m_whorliness, density
## 7: SWV, m_volume, m_a0, m_taper, m_ovality, m_whorliness, density
## 8: SWV, m_volume, m_a0, m_taper, m_ovality, m_whorliness, density, sweep.prod
## 9: SWV, m_volume, m_sed, m_a0, m_taper, m_ovality, m_whorliness, density, sweep.prod
## 10: SWV, m_volume, m_sed, m_a0, m_a1, m_taper, m_waist, m_ovality, m_whorliness, density
## 11: SWV, m_volume, m_sed, m_a0, m_a1, m_taper, m_waist, m_ovality, m_whorliness, density, sweep.prod
## 12: SWV, m_volume, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, density, sweep.prod
## 13: SWV, m_volume, m_led, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, density, sweep.prod
## 14: SWV, m_volume, m_led, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, density, hw.vfrac, sweep.prod
## 15: SWV, m_volume, m_led, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep2, density, hw.vfrac, sweep.prod
## 16: SWV, m_volume, m_led, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, density, hw.vfrac, sweep.prod
##
## twist_avg
## 1: SWV
## 2: SWV, m_led
## 3: SWV, m_led, density
## 4: SWV, m_led, m_sed, density
## 5: SWV, m_led, m_a0, m_taper, density
## 6: SWV, m_led, m_a2, m_ovality, m_sweep1, density
## 7: SWV, m_led, m_sed, m_taper, m_ovality, m_sweep1, density
## 8: SWV, m_led, m_sed, m_taper, m_ovality, m_whorliness, m_sweep1, density
## 9: SWV, m_volume, m_led, m_a0, m_a1, m_waist, m_ovality, m_sweep1, density
## 10: SWV, m_led, m_sed, m_taper, m_ovality, m_whorliness, m_sweep1, m_sweep2, density, sweep.prod
## 11: SWV, m_led, m_sed, m_a0, m_taper, m_ovality, m_whorliness, m_sweep1, m_sweep2, density, sweep.prod
## 12: SWV, m_volume, m_led, m_sed, m_a0, m_taper, m_ovality, m_whorliness, m_sweep1, m_sweep2, density, sweep.prod
## 13: SWV, m_volume, m_led, m_sed, m_a0, m_a1, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, density, sweep.prod
## 14: SWV, m_volume, m_led, m_sed, m_a0, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, density, sweep.prod
## 15: SWV, m_volume, m_led, m_sed, m_a0, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, density, hw.vfrac, sweep.prod
## 16: SWV, m_volume, m_led, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, density, hw.vfrac, sweep.prodNote: other R packages include bestglm, subselect, leaps, glmulti.
library(glmulti)## Loading required package: rJavalibrary(leaps)##
## Attaching package: 'leaps'
##
## The following object is masked from 'package:subselect':
##
## leapsLL <- L[,predictors]
LL$y = L$E.avg.dyn
m.0 <- glm(y ~ ., LL, family=gaussian(link="identity")) # equiv to a linear model
m.best <- glmulti(m.0, method="l", level=1) # level=1 forces no interactions## Initialization...
## TASK: Exhaustive screening of candidate set, branch-and-bound algorithm.
## [ Be sure to have package leaps installed ]
## Fitting...
## Completed.
## 163 first best models identified.# sloooooooow!From ML, May 8:
To do: * Any further models needed for warp? Why are models significantly poorer than KPP – due to compression wood outliers being removed in the KPP analysis? * Is a stiffness segregation device required on the edger? i.e. what % of lumber that is not from the cant has stiffness below 6GPa? If this is a major lets drop it. * Do we need to say anything about the mc data (NMI and aquascan)? A comment on importance of mc in warp expression and the variability we saw would be useful. * Any further implications for JNL trial?
2.3 Inner and Outer Models
2.3.1 MoE
grid.arrange(
xyplot(E.gradient ~ E.d.hitman_avg, L),
xyplot(E.gradient ~ volume, L),
ncol=2)
Reasonable range in inner:outer stiffness. Independent of overall stiffness and size.
See models below.
2.3.2 Crook
summary(m <- best.linear.model(L$crook_avg))##
## Call:
## lm(formula = y ~ density + sweep.prod + m_waist + m_a2, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2187 -1.4615 -0.2139 1.0972 10.3181
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.094252 2.899470 4.171 6.14e-05 ***
## density -0.008411 0.003038 -2.768 0.00663 **
## sweep.prod 2.042667 0.857806 2.381 0.01900 *
## m_waist -38.089688 25.865324 -1.473 0.14376
## m_a2 0.404822 0.283115 1.430 0.15564
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.165 on 108 degrees of freedom
## Multiple R-squared: 0.1638, Adjusted R-squared: 0.1328
## F-statistic: 5.288 on 4 and 108 DF, p-value: 0.0006287 summary(m <- best.linear.model(L$crook_avg_inner))
##
## Call:
## lm(formula = y ~ density + m_sed + m_a1, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.3515 -2.2370 -0.7273 1.3997 14.1913
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.207336 4.377100 3.246 0.00156 **
## density -0.014262 0.004726 -3.018 0.00318 **
## m_sed 0.012210 0.004824 2.531 0.01283 *
## m_a1 0.021119 0.010744 1.966 0.05192 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.338 on 107 degrees of freedom
## Multiple R-squared: 0.1264, Adjusted R-squared: 0.1019
## F-statistic: 5.159 on 3 and 107 DF, p-value: 0.002275 summary(m <- best.linear.model(L$crook_avg_outer))
##
## Call:
## lm(formula = y ~ SWV + m_waist + m_a2, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4509 -1.5887 -0.5386 1.0625 10.2911
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.942566 3.582819 3.333 0.00123 **
## SWV -0.002244 0.001086 -2.066 0.04159 *
## m_waist -53.844271 31.306135 -1.720 0.08874 .
## m_a2 0.574311 0.342203 1.678 0.09661 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.52 on 94 degrees of freedom
## Multiple R-squared: 0.09871, Adjusted R-squared: 0.06995
## F-statistic: 3.432 on 3 and 94 DF, p-value: 0.02015Best inner and outer models are both WORSE than total!
2.3.3 Bow
summary(m <- best.linear.model(L$bow_avg))##
## Call:
## lm(formula = y ~ SWV + density + m_whorliness + m_taper, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.5526 -2.1948 -0.5343 1.6360 15.9240
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 50.677145 8.169233 6.203 1.04e-08 ***
## SWV -0.006277 0.001527 -4.112 7.67e-05 ***
## density -0.016368 0.005161 -3.172 0.00197 **
## m_whorliness -2.400092 1.140879 -2.104 0.03772 *
## m_taper -0.233965 0.125925 -1.858 0.06590 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.566 on 108 degrees of freedom
## Multiple R-squared: 0.1962, Adjusted R-squared: 0.1664
## F-statistic: 6.589 on 4 and 108 DF, p-value: 8.771e-05 summary(m <- best.linear.model(L$bow_avg_inner))
##
## Call:
## lm(formula = y ~ SWV + m_ovality + density + m_whorliness + m_a1,
## data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.4893 -3.3069 -0.5184 2.3588 17.7155
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.634e+01 1.173e+01 4.802 5.24e-06 ***
## SWV -7.652e-03 2.182e-03 -3.507 0.000668 ***
## m_ovality 1.873e+03 7.077e+02 2.646 0.009390 **
## density -1.829e-02 7.345e-03 -2.490 0.014350 *
## m_whorliness -2.890e+00 1.624e+00 -1.780 0.077937 .
## m_a1 -2.640e-02 1.735e-02 -1.522 0.131065
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.082 on 105 degrees of freedom
## Multiple R-squared: 0.1844, Adjusted R-squared: 0.1456
## F-statistic: 4.749 on 5 and 105 DF, p-value: 0.0005952 summary(m <- best.linear.model(L$bow_avg_outer))
##
## Call:
## lm(formula = y ~ SWV + density + m_taper, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.946 -3.105 -0.955 2.327 29.900
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 75.091423 14.652257 5.125 1.59e-06 ***
## SWV -0.010613 0.002732 -3.884 0.000191 ***
## density -0.027779 0.009864 -2.816 0.005921 **
## m_taper -0.460498 0.208543 -2.208 0.029664 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.96 on 94 degrees of freedom
## Multiple R-squared: 0.1844, Adjusted R-squared: 0.1584
## F-statistic: 7.083 on 3 and 94 DF, p-value: 0.00024182.3.4 Outer c.f. Inner
Do logs with lousy inner boards also have lousy outer boards?
xyplot(crook_avg_outer ~ crook_avg_inner, L)
xyplot(bow_avg_outer ~ bow_avg_inner, L)
xyplot(twist_avg_outer ~ twist_avg_inner, L)
No. Outer and Inner pretty much uncorrelated.
2.4 Reduced Datasets
If we look only at subsets of logs that are in some way ‘good’, do we get similar prediction models?
2.4.1 Marco’s CW-free logs
Fit models only to logs Marco identified as not appearing to contain significant CW based on log end imagery.
both.ends.severe.cw = c(189, 210, 212, 222)
both.ends.moderate.cw = c(123, 135, 144, 159, 178, 188, 190, 208)
for (lmeas in log.quality.measures) {
models <- cf.full(lmeas, idx.sub=!L$SWILogNumber%in%union(both.ends.severe.cw,both.ends.moderate.cw))
}
Call:
lm(formula = y ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.6464 -0.4315 0.0184 0.4831 1.6250
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.305e+01 2.335e+00 -5.589 1.78e-07 ***
SWV 4.950e-03 3.376e-04 14.662 < 2e-16 ***
density 6.415e-03 1.885e-03 3.404 0.00094 ***
m_a2 6.667e-03 2.754e-03 2.421 0.01718 *
hw.vfrac -3.056e+00 1.266e+00 -2.413 0.01755 *
m_led 2.925e-03 1.113e-03 2.628 0.00987 **
m_whorliness -6.037e-01 2.500e-01 -2.414 0.01748 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7714 on 106 degrees of freedom
Multiple R-squared: 0.751, Adjusted R-squared: 0.7369
F-statistic: 53.27 on 6 and 106 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + m_waist + hw.vfrac + m_volume +
m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.3986 -0.4296 -0.0076 0.5089 1.6441
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.209e+01 2.321e+00 -5.211 1.09e-06 ***
SWV 4.632e-03 3.508e-04 13.201 < 2e-16 ***
density 6.840e-03 1.887e-03 3.625 0.000467 ***
m_waist 6.896e-01 2.452e-01 2.813 0.005964 **
hw.vfrac -2.357e+00 1.292e+00 -1.825 0.071213 .
m_volume 1.150e+00 4.480e-01 2.566 0.011842 *
m_whorliness -5.639e-01 2.567e-01 -2.196 0.030499 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7635 on 95 degrees of freedom
Multiple R-squared: 0.7374, Adjusted R-squared: 0.7208
F-statistic: 44.46 on 6 and 95 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.6464 -0.4315 0.0184 0.4831 1.6250
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.305e+01 2.335e+00 -5.589 1.78e-07 ***
SWV 4.950e-03 3.376e-04 14.662 < 2e-16 ***
density 6.415e-03 1.885e-03 3.404 0.00094 ***
m_a2 6.667e-03 2.754e-03 2.421 0.01718 *
hw.vfrac -3.056e+00 1.266e+00 -2.413 0.01755 *
m_led 2.925e-03 1.113e-03 2.628 0.00987 **
m_whorliness -6.037e-01 2.500e-01 -2.414 0.01748 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7714 on 106 degrees of freedom
Multiple R-squared: 0.751, Adjusted R-squared: 0.7369
F-statistic: 53.27 on 6 and 106 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + m_waist + hw.vfrac + m_volume +
m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.3986 -0.4296 -0.0076 0.5089 1.6441
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.209e+01 2.321e+00 -5.211 1.09e-06 ***
SWV 4.632e-03 3.508e-04 13.201 < 2e-16 ***
density 6.840e-03 1.887e-03 3.625 0.000467 ***
m_waist 6.896e-01 2.452e-01 2.813 0.005964 **
hw.vfrac -2.357e+00 1.292e+00 -1.825 0.071213 .
m_volume 1.150e+00 4.480e-01 2.566 0.011842 *
m_whorliness -5.639e-01 2.567e-01 -2.196 0.030499 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7635 on 95 degrees of freedom
Multiple R-squared: 0.7374, Adjusted R-squared: 0.7208
F-statistic: 44.46 on 6 and 95 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + m_a0 + m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.0982 -0.4582 -0.0849 0.6509 3.3074
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.068e+01 2.419e+00 -4.413 2.46e-05 ***
SWV 3.428e-03 4.446e-04 7.711 7.14e-12 ***
density 1.076e-02 1.555e-03 6.917 3.62e-10 ***
m_a0 -5.665e-03 1.569e-03 -3.610 0.00047 ***
m_whorliness -9.176e-01 3.384e-01 -2.712 0.00781 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.061 on 106 degrees of freedom
Multiple R-squared: 0.5625, Adjusted R-squared: 0.546
F-statistic: 34.07 on 4 and 106 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + m_a0 + m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-2.9400 -0.4667 -0.0582 0.5461 3.3404
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -9.5077282 2.5806155 -3.684 0.000381 ***
SWV 0.0032511 0.0004723 6.883 6.25e-10 ***
density 0.0100679 0.0016304 6.175 1.63e-08 ***
m_a0 -0.0060808 0.0016390 -3.710 0.000349 ***
m_whorliness -0.7949137 0.3615484 -2.199 0.030332 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.076 on 95 degrees of freedom
Multiple R-squared: 0.5388, Adjusted R-squared: 0.5193
F-statistic: 27.74 on 4 and 95 DF, p-value: 2.894e-15
|
Call:
lm(formula = y ~ SWV + density + m_a1 + m_sweep1 + m_ovality +
hw.vfrac, data = LL)
Residuals:
Min 1Q Median 3Q Max
-5.4303 -0.5042 -0.1239 0.6437 3.6348
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.416e+01 4.047e+00 -3.498 0.000733 ***
SWV 6.299e-03 5.837e-04 10.792 < 2e-16 ***
density 6.008e-03 3.373e-03 1.781 0.078294 .
m_a1 -1.493e-02 4.419e-03 -3.378 0.001084 **
m_sweep1 -4.222e-01 2.125e-01 -1.987 0.050044 .
m_ovality 3.387e+02 1.789e+02 1.893 0.061576 .
hw.vfrac -3.649e+00 2.202e+00 -1.657 0.100962
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.248 on 89 degrees of freedom
Multiple R-squared: 0.66, Adjusted R-squared: 0.637
F-statistic: 28.79 on 6 and 89 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + m_a1 + m_sweep1 + m_ovality,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-5.0048 -0.6420 -0.0401 0.7925 2.7601
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.576e+01 3.307e+00 -4.767 8.39e-06 ***
SWV 5.639e-03 5.946e-04 9.484 1.11e-14 ***
density 9.172e-03 2.190e-03 4.188 7.26e-05 ***
m_a1 -1.367e-02 4.421e-03 -3.092 0.00274 **
m_sweep1 -5.185e-01 2.261e-01 -2.293 0.02448 *
m_ovality 3.356e+02 1.887e+02 1.778 0.07927 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.23 on 79 degrees of freedom
Multiple R-squared: 0.6361, Adjusted R-squared: 0.6131
F-statistic: 27.62 on 5 and 79 DF, p-value: 4.601e-16
|
Call:
lm(formula = y ~ SWV + m_a0 + weight + m_sweep1 + m_ovality +
m_a1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.28074 -0.08097 -0.00794 0.06625 0.42431
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.509e+00 2.814e-01 5.361 6.58e-07 ***
SWV -1.081e-04 6.759e-05 -1.599 0.1135
m_a0 -2.265e-03 1.168e-03 -1.939 0.0557 .
weight 6.249e-04 4.242e-04 1.473 0.1443
m_sweep1 5.241e-02 2.294e-02 2.285 0.0247 *
m_ovality -3.539e+01 1.939e+01 -1.825 0.0714 .
m_a1 8.768e-04 5.197e-04 1.687 0.0951 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1351 on 88 degrees of freedom
Multiple R-squared: 0.2593, Adjusted R-squared: 0.2088
F-statistic: 5.134 on 6 and 88 DF, p-value: 0.0001451
|
Call:
lm(formula = y ~ SWV + m_a0 + m_sweep1 + m_ovality + m_a1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.27949 -0.08773 -0.01653 0.07307 0.42331
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.315e+00 2.753e-01 4.778 8.18e-06 ***
SWV -1.296e-04 6.901e-05 -1.877 0.0642 .
m_a0 -6.116e-04 2.408e-04 -2.539 0.0131 *
m_sweep1 5.495e-02 2.578e-02 2.132 0.0362 *
m_ovality -4.272e+01 2.151e+01 -1.986 0.0505 .
m_a1 1.011e-03 5.231e-04 1.933 0.0569 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.14 on 78 degrees of freedom
Multiple R-squared: 0.2507, Adjusted R-squared: 0.2027
F-statistic: 5.219 on 5 and 78 DF, p-value: 0.000348
|
Call:
lm(formula = y ~ density + sweep.prod + m_waist + m_a2, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.2187 -1.4615 -0.2139 1.0972 10.3181
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.094252 2.899470 4.171 6.14e-05 ***
density -0.008411 0.003038 -2.768 0.00663 **
sweep.prod 2.042667 0.857806 2.381 0.01900 *
m_waist -38.089688 25.865324 -1.473 0.14376
m_a2 0.404822 0.283115 1.430 0.15564
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.165 on 108 degrees of freedom
Multiple R-squared: 0.1638, Adjusted R-squared: 0.1328
F-statistic: 5.288 on 4 and 108 DF, p-value: 0.0006287
|
Call:
lm(formula = y ~ SWV + density + sweep.prod, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.2856 -1.1489 -0.3096 0.8675 7.0781
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 19.5165707 4.1852494 4.663 9.85e-06 ***
SWV -0.0017289 0.0007673 -2.253 0.026466 *
density -0.0103478 0.0026784 -3.863 0.000201 ***
sweep.prod 1.3387237 0.7619663 1.757 0.082053 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.755 on 98 degrees of freedom
Multiple R-squared: 0.2072, Adjusted R-squared: 0.1829
F-statistic: 8.536 on 3 and 98 DF, p-value: 4.311e-05
|
Call:
lm(formula = y ~ density + m_sed + m_a1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-6.3515 -2.2370 -0.7273 1.3997 14.1913
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.207336 4.377100 3.246 0.00156 **
density -0.014262 0.004726 -3.018 0.00318 **
m_sed 0.012210 0.004824 2.531 0.01283 *
m_a1 0.021119 0.010744 1.966 0.05192 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.338 on 107 degrees of freedom
Multiple R-squared: 0.1264, Adjusted R-squared: 0.1019
F-statistic: 5.159 on 3 and 107 DF, p-value: 0.002275
|
Call:
lm(formula = y ~ SWV + density + m_sed + m_a1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-5.555 -2.050 -0.553 1.553 9.917
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 20.786869 7.040051 2.953 0.00397 **
SWV -0.001898 0.001325 -1.432 0.15550
density -0.014302 0.004364 -3.277 0.00146 **
m_sed 0.011372 0.004470 2.544 0.01257 *
m_a1 0.015003 0.010186 1.473 0.14408
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.911 on 95 degrees of freedom
Multiple R-squared: 0.1721, Adjusted R-squared: 0.1373
F-statistic: 4.938 on 4 and 95 DF, p-value: 0.001164
|
Call:
lm(formula = y ~ SWV + m_waist + m_a2, data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.4509 -1.5887 -0.5386 1.0625 10.2911
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.942566 3.582819 3.333 0.00123 **
SWV -0.002244 0.001086 -2.066 0.04159 *
m_waist -53.844271 31.306135 -1.720 0.08874 .
m_a2 0.574311 0.342203 1.678 0.09661 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.52 on 94 degrees of freedom
Multiple R-squared: 0.09871, Adjusted R-squared: 0.06995
F-statistic: 3.432 on 3 and 94 DF, p-value: 0.02015
|
Call:
lm(formula = y ~ SWV + m_waist + m_a2, data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.2420 -1.4535 -0.3935 1.1775 10.0626
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.61436 3.35534 4.058 0.000112 ***
SWV -0.00283 0.00102 -2.775 0.006824 **
m_waist -57.33523 29.50023 -1.944 0.055338 .
m_a2 0.61452 0.32222 1.907 0.059961 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.26 on 83 degrees of freedom
Multiple R-squared: 0.1442, Adjusted R-squared: 0.1133
F-statistic: 4.661 on 3 and 83 DF, p-value: 0.004638
|
Call:
lm(formula = y ~ SWV + density + m_whorliness + m_taper, data = LL)
Residuals:
Min 1Q Median 3Q Max
-8.5526 -2.1948 -0.5343 1.6360 15.9240
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 50.677145 8.169233 6.203 1.04e-08 ***
SWV -0.006277 0.001527 -4.112 7.67e-05 ***
density -0.016368 0.005161 -3.172 0.00197 **
m_whorliness -2.400092 1.140879 -2.104 0.03772 *
m_taper -0.233965 0.125925 -1.858 0.06590 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.566 on 108 degrees of freedom
Multiple R-squared: 0.1962, Adjusted R-squared: 0.1664
F-statistic: 6.589 on 4 and 108 DF, p-value: 8.771e-05
|
Call:
lm(formula = y ~ SWV + m_taper + density + m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-8.4203 -2.1292 -0.4479 1.6823 10.0999
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 47.883409 7.923471 6.043 2.80e-08 ***
SWV -0.006097 0.001469 -4.150 7.15e-05 ***
m_taper -0.292561 0.120976 -2.418 0.01746 *
density -0.013914 0.004936 -2.819 0.00584 **
m_whorliness -2.011714 1.100331 -1.828 0.07058 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.295 on 97 degrees of freedom
Multiple R-squared: 0.2117, Adjusted R-squared: 0.1792
F-statistic: 6.512 on 4 and 97 DF, p-value: 0.0001101
|
Call:
lm(formula = y ~ SWV + m_ovality + density + m_whorliness + m_a1,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-9.4893 -3.3069 -0.5184 2.3588 17.7155
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.634e+01 1.173e+01 4.802 5.24e-06 ***
SWV -7.652e-03 2.182e-03 -3.507 0.000668 ***
m_ovality 1.873e+03 7.077e+02 2.646 0.009390 **
density -1.829e-02 7.345e-03 -2.490 0.014350 *
m_whorliness -2.890e+00 1.624e+00 -1.780 0.077937 .
m_a1 -2.640e-02 1.735e-02 -1.522 0.131065
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.082 on 105 degrees of freedom
Multiple R-squared: 0.1844, Adjusted R-squared: 0.1456
F-statistic: 4.749 on 5 and 105 DF, p-value: 0.0005952
|
Call:
lm(formula = y ~ SWV + m_whorliness + density, data = LL)
Residuals:
Min 1Q Median 3Q Max
-10.9766 -3.1587 -0.8173 2.3663 17.7707
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 50.137584 11.734963 4.272 4.55e-05 ***
SWV -0.006624 0.002152 -3.078 0.00272 **
m_whorliness -3.404704 1.619691 -2.102 0.03816 *
density -0.015122 0.007399 -2.044 0.04370 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.947 on 96 degrees of freedom
Multiple R-squared: 0.1155, Adjusted R-squared: 0.0879
F-statistic: 4.18 on 3 and 96 DF, p-value: 0.007925
|
Call:
lm(formula = y ~ SWV + density + m_taper, data = LL)
Residuals:
Min 1Q Median 3Q Max
-9.946 -3.105 -0.955 2.327 29.900
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 75.091423 14.652257 5.125 1.59e-06 ***
SWV -0.010613 0.002732 -3.884 0.000191 ***
density -0.027779 0.009864 -2.816 0.005921 **
m_taper -0.460498 0.208543 -2.208 0.029664 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.96 on 94 degrees of freedom
Multiple R-squared: 0.1844, Adjusted R-squared: 0.1584
F-statistic: 7.083 on 3 and 94 DF, p-value: 0.0002418
|
Call:
lm(formula = y ~ SWV + density + m_taper, data = LL)
Residuals:
Min 1Q Median 3Q Max
-9.753 -3.667 -1.225 2.519 30.211
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 72.023701 15.845458 4.545 1.85e-05 ***
SWV -0.010317 0.002927 -3.525 0.000692 ***
density -0.025222 0.010563 -2.388 0.019221 *
m_taper -0.499506 0.222660 -2.243 0.027538 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.105 on 83 degrees of freedom
Multiple R-squared: 0.1737, Adjusted R-squared: 0.1438
F-statistic: 5.816 on 3 and 83 DF, p-value: 0.001172
|
Call:
lm(formula = y ~ SWV + m_led + density + m_sed, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.4416 -1.2848 0.0444 1.0252 4.1047
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.2602020 3.5839449 4.258 4.42e-05 ***
SWV -0.0041115 0.0006817 -6.031 2.32e-08 ***
m_led -0.0285152 0.0097001 -2.940 0.00402 **
density 0.0071756 0.0023647 3.035 0.00302 **
m_sed 0.0155077 0.0104349 1.486 0.14016
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.634 on 108 degrees of freedom
Multiple R-squared: 0.4176, Adjusted R-squared: 0.396
F-statistic: 19.36 on 4 and 108 DF, p-value: 4.946e-12
|
Call:
lm(formula = y ~ SWV + m_led + density + m_sed, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.5245 -1.3715 0.1127 0.9839 4.0413
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.3656349 3.8319627 4.010 0.000119 ***
SWV -0.0040186 0.0007231 -5.558 2.4e-07 ***
m_led -0.0315706 0.0103533 -3.049 0.002957 **
density 0.0070289 0.0024901 2.823 0.005778 **
m_sed 0.0182831 0.0110850 1.649 0.102309
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.665 on 97 degrees of freedom
Multiple R-squared: 0.4316, Adjusted R-squared: 0.4082
F-statistic: 18.41 on 4 and 97 DF, p-value: 2.762e-11
|
Call:
lm(formula = y ~ SWV + m_led + density + m_sed + m_sweep1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.576 -1.782 -0.099 1.494 5.508
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.3355124 5.0898748 2.620 0.010071 *
SWV -0.0039981 0.0009606 -4.162 6.40e-05 ***
m_led -0.0527561 0.0137747 -3.830 0.000216 ***
density 0.0138311 0.0033452 4.135 7.09e-05 ***
m_sed 0.0364908 0.0148036 2.465 0.015292 *
m_sweep1 -0.5561260 0.3520536 -1.580 0.117136
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.302 on 107 degrees of freedom
Multiple R-squared: 0.4114, Adjusted R-squared: 0.3839
F-statistic: 14.96 on 5 and 107 DF, p-value: 4.049e-11
|
Call:
lm(formula = y ~ SWV + m_led + density + m_sed, data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.4788 -1.9815 -0.1636 1.5593 5.0716
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.2642610 5.2945580 1.939 0.055451 .
SWV -0.0034764 0.0009991 -3.480 0.000754 ***
m_led -0.0590081 0.0143050 -4.125 7.83e-05 ***
density 0.0150391 0.0034406 4.371 3.11e-05 ***
m_sed 0.0423096 0.0153159 2.762 0.006864 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.3 on 97 degrees of freedom
Multiple R-squared: 0.4253, Adjusted R-squared: 0.4016
F-statistic: 17.95 on 4 and 97 DF, p-value: 4.659e-11
|
2.4.2 JH ‘well-represented’ logs
- chosen by vfrac rec
rec = L$nboards.complete/L$volume
for (lmeas in log.quality.measures) {
models <- cf.full(lmeas, idx.sub=rec>quantile(rec, 0.8))
}
Call:
lm(formula = y ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.6464 -0.4315 0.0184 0.4831 1.6250
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.305e+01 2.335e+00 -5.589 1.78e-07 ***
SWV 4.950e-03 3.376e-04 14.662 < 2e-16 ***
density 6.415e-03 1.885e-03 3.404 0.00094 ***
m_a2 6.667e-03 2.754e-03 2.421 0.01718 *
hw.vfrac -3.056e+00 1.266e+00 -2.413 0.01755 *
m_led 2.925e-03 1.113e-03 2.628 0.00987 **
m_whorliness -6.037e-01 2.500e-01 -2.414 0.01748 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7714 on 106 degrees of freedom
Multiple R-squared: 0.751, Adjusted R-squared: 0.7369
F-statistic: 53.27 on 6 and 106 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + m_a2 + m_volume, data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.7742 -0.3067 0.1119 0.2132 0.8141
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.181e+01 2.237e+00 -9.749 4.84e-09 ***
SWV 7.066e-03 4.841e-04 14.596 3.98e-12 ***
density 7.676e-03 1.685e-03 4.557 0.000192 ***
m_a2 1.641e-02 4.377e-03 3.748 0.001266 **
m_volume 1.923e+00 6.494e-01 2.961 0.007716 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4415 on 20 degrees of freedom
Multiple R-squared: 0.9345, Adjusted R-squared: 0.9214
F-statistic: 71.36 on 4 and 20 DF, p-value: 1.499e-11
|
Call:
lm(formula = y ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.6464 -0.4315 0.0184 0.4831 1.6250
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.305e+01 2.335e+00 -5.589 1.78e-07 ***
SWV 4.950e-03 3.376e-04 14.662 < 2e-16 ***
density 6.415e-03 1.885e-03 3.404 0.00094 ***
m_a2 6.667e-03 2.754e-03 2.421 0.01718 *
hw.vfrac -3.056e+00 1.266e+00 -2.413 0.01755 *
m_led 2.925e-03 1.113e-03 2.628 0.00987 **
m_whorliness -6.037e-01 2.500e-01 -2.414 0.01748 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7714 on 106 degrees of freedom
Multiple R-squared: 0.751, Adjusted R-squared: 0.7369
F-statistic: 53.27 on 6 and 106 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + m_a2 + m_volume, data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.7742 -0.3067 0.1119 0.2132 0.8141
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.181e+01 2.237e+00 -9.749 4.84e-09 ***
SWV 7.066e-03 4.841e-04 14.596 3.98e-12 ***
density 7.676e-03 1.685e-03 4.557 0.000192 ***
m_a2 1.641e-02 4.377e-03 3.748 0.001266 **
m_volume 1.923e+00 6.494e-01 2.961 0.007716 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4415 on 20 degrees of freedom
Multiple R-squared: 0.9345, Adjusted R-squared: 0.9214
F-statistic: 71.36 on 4 and 20 DF, p-value: 1.499e-11
|
Call:
lm(formula = y ~ SWV + density + m_a0 + m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.0982 -0.4582 -0.0849 0.6509 3.3074
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.068e+01 2.419e+00 -4.413 2.46e-05 ***
SWV 3.428e-03 4.446e-04 7.711 7.14e-12 ***
density 1.076e-02 1.555e-03 6.917 3.62e-10 ***
m_a0 -5.665e-03 1.569e-03 -3.610 0.00047 ***
m_whorliness -9.176e-01 3.384e-01 -2.712 0.00781 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.061 on 106 degrees of freedom
Multiple R-squared: 0.5625, Adjusted R-squared: 0.546
F-statistic: 34.07 on 4 and 106 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + hw.vfrac + m_a0 + m_sed + sweep.prod +
m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-1.0563 -0.2797 -0.1609 0.2515 1.0590
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.904e-01 2.971e+00 0.098 0.92322
SWV 3.719e-03 6.604e-04 5.632 2.42e-05 ***
hw.vfrac -1.142e+01 1.933e+00 -5.911 1.35e-05 ***
m_a0 -8.106e-02 2.514e-02 -3.224 0.00471 **
m_sed 7.522e-02 2.480e-02 3.033 0.00715 **
sweep.prod -1.190e+00 4.602e-01 -2.586 0.01864 *
m_whorliness 1.012e+00 6.471e-01 1.564 0.13513
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5764 on 18 degrees of freedom
Multiple R-squared: 0.8661, Adjusted R-squared: 0.8215
F-statistic: 19.41 on 6 and 18 DF, p-value: 5.886e-07
|
Call:
lm(formula = y ~ SWV + density + m_a1 + m_sweep1 + m_ovality +
hw.vfrac, data = LL)
Residuals:
Min 1Q Median 3Q Max
-5.4303 -0.5042 -0.1239 0.6437 3.6348
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.416e+01 4.047e+00 -3.498 0.000733 ***
SWV 6.299e-03 5.837e-04 10.792 < 2e-16 ***
density 6.008e-03 3.373e-03 1.781 0.078294 .
m_a1 -1.493e-02 4.419e-03 -3.378 0.001084 **
m_sweep1 -4.222e-01 2.125e-01 -1.987 0.050044 .
m_ovality 3.387e+02 1.789e+02 1.893 0.061576 .
hw.vfrac -3.649e+00 2.202e+00 -1.657 0.100962
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.248 on 89 degrees of freedom
Multiple R-squared: 0.66, Adjusted R-squared: 0.637
F-statistic: 28.79 on 6 and 89 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + m_a2 + density + m_waist + weight + m_sweep2 +
m_sed + m_led + m_taper, data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.8769 -0.3370 -0.1419 0.2921 1.2454
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.400e+01 7.587e+00 -4.481 0.000518 ***
SWV 8.944e-03 8.779e-04 10.188 7.41e-08 ***
m_a2 6.802e-01 3.208e-01 2.121 0.052301 .
density 8.392e-03 4.337e-03 1.935 0.073459 .
m_waist -6.049e+01 2.923e+01 -2.069 0.057514 .
weight -1.260e-02 7.514e-03 -1.677 0.115709
m_sweep2 -4.223e+00 1.848e+00 -2.285 0.038394 *
m_sed -3.291e-02 2.654e-02 -1.240 0.235380
m_led 7.504e-02 2.451e-02 3.062 0.008446 **
m_taper -3.133e-01 1.400e-01 -2.237 0.042044 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6828 on 14 degrees of freedom
Multiple R-squared: 0.9469, Adjusted R-squared: 0.9128
F-statistic: 27.75 on 9 and 14 DF, p-value: 2.041e-07
|
Call:
lm(formula = y ~ SWV + m_a0 + weight + m_sweep1 + m_ovality +
m_a1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.28074 -0.08097 -0.00794 0.06625 0.42431
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.509e+00 2.814e-01 5.361 6.58e-07 ***
SWV -1.081e-04 6.759e-05 -1.599 0.1135
m_a0 -2.265e-03 1.168e-03 -1.939 0.0557 .
weight 6.249e-04 4.242e-04 1.473 0.1443
m_sweep1 5.241e-02 2.294e-02 2.285 0.0247 *
m_ovality -3.539e+01 1.939e+01 -1.825 0.0714 .
m_a1 8.768e-04 5.197e-04 1.687 0.0951 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1351 on 88 degrees of freedom
Multiple R-squared: 0.2593, Adjusted R-squared: 0.2088
F-statistic: 5.134 on 6 and 88 DF, p-value: 0.0001451
|
Call:
lm(formula = y ~ SWV + m_waist + m_a0 + weight, data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.140719 -0.066387 0.009874 0.047627 0.203043
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.0844626 0.4696922 6.567 2.75e-06 ***
SWV -0.0003133 0.0001005 -3.116 0.00569 **
m_waist -0.1469892 0.0815734 -1.802 0.08745 .
m_a0 -0.0066717 0.0019244 -3.467 0.00258 **
weight 0.0020403 0.0006679 3.055 0.00652 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.09244 on 19 degrees of freedom
Multiple R-squared: 0.6114, Adjusted R-squared: 0.5296
F-statistic: 7.474 on 4 and 19 DF, p-value: 0.0008573
|
Call:
lm(formula = y ~ density + sweep.prod + m_waist + m_a2, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.2187 -1.4615 -0.2139 1.0972 10.3181
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.094252 2.899470 4.171 6.14e-05 ***
density -0.008411 0.003038 -2.768 0.00663 **
sweep.prod 2.042667 0.857806 2.381 0.01900 *
m_waist -38.089688 25.865324 -1.473 0.14376
m_a2 0.404822 0.283115 1.430 0.15564
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.165 on 108 degrees of freedom
Multiple R-squared: 0.1638, Adjusted R-squared: 0.1328
F-statistic: 5.288 on 4 and 108 DF, p-value: 0.0006287
|
Call:
lm(formula = y ~ 1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-2.7292 -1.8292 -0.3007 0.7708 11.8958
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.7292 0.5789 8.17 2.17e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.894 on 24 degrees of freedom
|
Call:
lm(formula = y ~ density + m_sed + m_a1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-6.3515 -2.2370 -0.7273 1.3997 14.1913
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.207336 4.377100 3.246 0.00156 **
density -0.014262 0.004726 -3.018 0.00318 **
m_sed 0.012210 0.004824 2.531 0.01283 *
m_a1 0.021119 0.010744 1.966 0.05192 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.338 on 107 degrees of freedom
Multiple R-squared: 0.1264, Adjusted R-squared: 0.1019
F-statistic: 5.159 on 3 and 107 DF, p-value: 0.002275
|
Call:
lm(formula = y ~ m_sed + weight, data = LL)
Residuals:
Min 1Q Median 3Q Max
-7.4666 -1.9054 0.0122 1.4801 11.1257
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -35.13091 11.48774 -3.058 0.00576 **
m_sed 0.22347 0.06781 3.296 0.00330 **
weight -0.06832 0.02318 -2.947 0.00745 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.465 on 22 degrees of freedom
Multiple R-squared: 0.4061, Adjusted R-squared: 0.3521
F-statistic: 7.522 on 2 and 22 DF, p-value: 0.003241
|
Call:
lm(formula = y ~ SWV + m_waist + m_a2, data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.4509 -1.5887 -0.5386 1.0625 10.2911
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.942566 3.582819 3.333 0.00123 **
SWV -0.002244 0.001086 -2.066 0.04159 *
m_waist -53.844271 31.306135 -1.720 0.08874 .
m_a2 0.574311 0.342203 1.678 0.09661 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.52 on 94 degrees of freedom
Multiple R-squared: 0.09871, Adjusted R-squared: 0.06995
F-statistic: 3.432 on 3 and 94 DF, p-value: 0.02015
|
Call:
lm(formula = y ~ 1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-2.5028 -1.5028 -0.6028 0.8514 5.4972
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.5028 0.4242 10.61 2.45e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.078 on 23 degrees of freedom
|
Call:
lm(formula = y ~ SWV + density + m_whorliness + m_taper, data = LL)
Residuals:
Min 1Q Median 3Q Max
-8.5526 -2.1948 -0.5343 1.6360 15.9240
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 50.677145 8.169233 6.203 1.04e-08 ***
SWV -0.006277 0.001527 -4.112 7.67e-05 ***
density -0.016368 0.005161 -3.172 0.00197 **
m_whorliness -2.400092 1.140879 -2.104 0.03772 *
m_taper -0.233965 0.125925 -1.858 0.06590 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.566 on 108 degrees of freedom
Multiple R-squared: 0.1962, Adjusted R-squared: 0.1664
F-statistic: 6.589 on 4 and 108 DF, p-value: 8.771e-05
|
Call:
lm(formula = y ~ SWV + weight, data = LL)
Residuals:
Min 1Q Median 3Q Max
-5.9225 -1.2890 -0.0818 1.2802 6.4691
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 49.658489 10.611518 4.680 0.000115 ***
SWV -0.010896 0.002933 -3.715 0.001204 **
weight -0.007575 0.003606 -2.101 0.047341 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.772 on 22 degrees of freedom
Multiple R-squared: 0.387, Adjusted R-squared: 0.3312
F-statistic: 6.943 on 2 and 22 DF, p-value: 0.004597
|
Call:
lm(formula = y ~ SWV + m_ovality + density + m_whorliness + m_a1,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-9.4893 -3.3069 -0.5184 2.3588 17.7155
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.634e+01 1.173e+01 4.802 5.24e-06 ***
SWV -7.652e-03 2.182e-03 -3.507 0.000668 ***
m_ovality 1.873e+03 7.077e+02 2.646 0.009390 **
density -1.829e-02 7.345e-03 -2.490 0.014350 *
m_whorliness -2.890e+00 1.624e+00 -1.780 0.077937 .
m_a1 -2.640e-02 1.735e-02 -1.522 0.131065
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.082 on 105 degrees of freedom
Multiple R-squared: 0.1844, Adjusted R-squared: 0.1456
F-statistic: 4.749 on 5 and 105 DF, p-value: 0.0005952
|
Call:
lm(formula = y ~ SWV + weight, data = LL)
Residuals:
Min 1Q Median 3Q Max
-7.4272 -2.4475 0.3669 2.5876 9.4585
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 63.664241 17.805276 3.576 0.00169 **
SWV -0.014296 0.004921 -2.905 0.00820 **
weight -0.012054 0.006050 -1.992 0.05888 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.651 on 22 degrees of freedom
Multiple R-squared: 0.2785, Adjusted R-squared: 0.2129
F-statistic: 4.246 on 2 and 22 DF, p-value: 0.02758
|
Call:
lm(formula = y ~ SWV + density + m_taper, data = LL)
Residuals:
Min 1Q Median 3Q Max
-9.946 -3.105 -0.955 2.327 29.900
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 75.091423 14.652257 5.125 1.59e-06 ***
SWV -0.010613 0.002732 -3.884 0.000191 ***
density -0.027779 0.009864 -2.816 0.005921 **
m_taper -0.460498 0.208543 -2.208 0.029664 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.96 on 94 degrees of freedom
Multiple R-squared: 0.1844, Adjusted R-squared: 0.1584
F-statistic: 7.083 on 3 and 94 DF, p-value: 0.0002418
|
Call:
lm(formula = y ~ SWV + sweep.prod + m_led + m_a0 + m_ovality +
m_a1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.5034 -1.7970 0.1555 1.5178 6.0262
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.337e+01 1.200e+01 2.780 0.012821 *
SWV -8.818e-03 2.932e-03 -3.008 0.007923 **
sweep.prod 7.736e+00 1.938e+00 3.992 0.000943 ***
m_led -2.274e-01 5.312e-02 -4.281 0.000506 ***
m_a0 2.556e-01 5.490e-02 4.656 0.000226 ***
m_ovality 1.783e+03 9.787e+02 1.822 0.086049 .
m_a1 4.833e-02 2.230e-02 2.167 0.044723 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.564 on 17 degrees of freedom
Multiple R-squared: 0.7339, Adjusted R-squared: 0.64
F-statistic: 7.815 on 6 and 17 DF, p-value: 0.0003758
|
Call:
lm(formula = y ~ SWV + m_led + density + m_sed, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.4416 -1.2848 0.0444 1.0252 4.1047
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.2602020 3.5839449 4.258 4.42e-05 ***
SWV -0.0041115 0.0006817 -6.031 2.32e-08 ***
m_led -0.0285152 0.0097001 -2.940 0.00402 **
density 0.0071756 0.0023647 3.035 0.00302 **
m_sed 0.0155077 0.0104349 1.486 0.14016
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.634 on 108 degrees of freedom
Multiple R-squared: 0.4176, Adjusted R-squared: 0.396
F-statistic: 19.36 on 4 and 108 DF, p-value: 4.946e-12
|
Call:
lm(formula = y ~ SWV + m_volume + weight, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.2678 -0.7506 0.1529 1.0158 2.3821
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 20.354767 5.686286 3.580 0.00177 **
SWV -0.004499 0.001560 -2.884 0.00889 **
m_volume -28.560019 14.396247 -1.984 0.06050 .
weight 0.022516 0.013175 1.709 0.10218
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.471 on 21 degrees of freedom
Multiple R-squared: 0.355, Adjusted R-squared: 0.2629
F-statistic: 3.853 on 3 and 21 DF, p-value: 0.02423
|
Call:
lm(formula = y ~ SWV + m_led + density + m_sed + m_sweep1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.576 -1.782 -0.099 1.494 5.508
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.3355124 5.0898748 2.620 0.010071 *
SWV -0.0039981 0.0009606 -4.162 6.40e-05 ***
m_led -0.0527561 0.0137747 -3.830 0.000216 ***
density 0.0138311 0.0033452 4.135 7.09e-05 ***
m_sed 0.0364908 0.0148036 2.465 0.015292 *
m_sweep1 -0.5561260 0.3520536 -1.580 0.117136
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.302 on 107 degrees of freedom
Multiple R-squared: 0.4114, Adjusted R-squared: 0.3839
F-statistic: 14.96 on 5 and 107 DF, p-value: 4.049e-11
|
Call:
lm(formula = y ~ SWV + m_volume + weight + m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.9754 -1.0723 -0.4065 1.5222 5.7799
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 40.058243 10.742915 3.729 0.00133 **
SWV -0.008161 0.002730 -2.990 0.00724 **
m_volume -56.914548 24.163128 -2.355 0.02882 *
weight 0.043924 0.022111 1.986 0.06085 .
m_whorliness -4.389246 2.604434 -1.685 0.10747
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.468 on 20 degrees of freedom
Multiple R-squared: 0.4167, Adjusted R-squared: 0.3
F-statistic: 3.571 on 4 and 20 DF, p-value: 0.02357
|
- Chosen by visual inspection of saw patterns
xyplot(rec ~ volume, L, group=SWILogNumber%in%near.complete.sawpatterns)
for (lmeas in log.quality.measures) {
models <- cf.full(lmeas, idx.sub=L$SWILogNumber%in%near.complete.sawpatterns)
}
Call:
lm(formula = y ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.6464 -0.4315 0.0184 0.4831 1.6250
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.305e+01 2.335e+00 -5.589 1.78e-07 ***
SWV 4.950e-03 3.376e-04 14.662 < 2e-16 ***
density 6.415e-03 1.885e-03 3.404 0.00094 ***
m_a2 6.667e-03 2.754e-03 2.421 0.01718 *
hw.vfrac -3.056e+00 1.266e+00 -2.413 0.01755 *
m_led 2.925e-03 1.113e-03 2.628 0.00987 **
m_whorliness -6.037e-01 2.500e-01 -2.414 0.01748 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7714 on 106 degrees of freedom
Multiple R-squared: 0.751, Adjusted R-squared: 0.7369
F-statistic: 53.27 on 6 and 106 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + weight + m_a0, data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.70670 -0.18717 0.09253 0.29746 0.49427
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.005e+01 3.183e+00 -6.299 1.06e-05 ***
SWV 6.661e-03 4.793e-04 13.900 2.38e-10 ***
density 9.728e-03 2.164e-03 4.495 0.000367 ***
weight 6.061e-03 2.762e-03 2.195 0.043275 *
m_a0 -1.323e-02 7.705e-03 -1.717 0.105287
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3604 on 16 degrees of freedom
Multiple R-squared: 0.9514, Adjusted R-squared: 0.9393
F-statistic: 78.34 on 4 and 16 DF, p-value: 2.67e-10
|
Call:
lm(formula = y ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.6464 -0.4315 0.0184 0.4831 1.6250
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.305e+01 2.335e+00 -5.589 1.78e-07 ***
SWV 4.950e-03 3.376e-04 14.662 < 2e-16 ***
density 6.415e-03 1.885e-03 3.404 0.00094 ***
m_a2 6.667e-03 2.754e-03 2.421 0.01718 *
hw.vfrac -3.056e+00 1.266e+00 -2.413 0.01755 *
m_led 2.925e-03 1.113e-03 2.628 0.00987 **
m_whorliness -6.037e-01 2.500e-01 -2.414 0.01748 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7714 on 106 degrees of freedom
Multiple R-squared: 0.751, Adjusted R-squared: 0.7369
F-statistic: 53.27 on 6 and 106 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + weight + m_a0, data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.70670 -0.18717 0.09253 0.29746 0.49427
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.005e+01 3.183e+00 -6.299 1.06e-05 ***
SWV 6.661e-03 4.793e-04 13.900 2.38e-10 ***
density 9.728e-03 2.164e-03 4.495 0.000367 ***
weight 6.061e-03 2.762e-03 2.195 0.043275 *
m_a0 -1.323e-02 7.705e-03 -1.717 0.105287
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3604 on 16 degrees of freedom
Multiple R-squared: 0.9514, Adjusted R-squared: 0.9393
F-statistic: 78.34 on 4 and 16 DF, p-value: 2.67e-10
|
Call:
lm(formula = y ~ SWV + density + m_a0 + m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.0982 -0.4582 -0.0849 0.6509 3.3074
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.068e+01 2.419e+00 -4.413 2.46e-05 ***
SWV 3.428e-03 4.446e-04 7.711 7.14e-12 ***
density 1.076e-02 1.555e-03 6.917 3.62e-10 ***
m_a0 -5.665e-03 1.569e-03 -3.610 0.00047 ***
m_whorliness -9.176e-01 3.384e-01 -2.712 0.00781 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.061 on 106 degrees of freedom
Multiple R-squared: 0.5625, Adjusted R-squared: 0.546
F-statistic: 34.07 on 4 and 106 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + m_a0, data = LL)
Residuals:
Min 1Q Median 3Q Max
-1.1435 -0.5177 -0.2974 0.2317 3.1009
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -13.221614 6.788385 -1.948 0.06816 .
SWV 0.004603 0.001351 3.407 0.00336 **
density 0.009881 0.005123 1.929 0.07062 .
m_a0 -0.008189 0.004271 -1.917 0.07214 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.024 on 17 degrees of freedom
Multiple R-squared: 0.7286, Adjusted R-squared: 0.6807
F-statistic: 15.22 on 3 and 17 DF, p-value: 4.574e-05
|
Call:
lm(formula = y ~ SWV + density + m_a1 + m_sweep1 + m_ovality +
hw.vfrac, data = LL)
Residuals:
Min 1Q Median 3Q Max
-5.4303 -0.5042 -0.1239 0.6437 3.6348
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.416e+01 4.047e+00 -3.498 0.000733 ***
SWV 6.299e-03 5.837e-04 10.792 < 2e-16 ***
density 6.008e-03 3.373e-03 1.781 0.078294 .
m_a1 -1.493e-02 4.419e-03 -3.378 0.001084 **
m_sweep1 -4.222e-01 2.125e-01 -1.987 0.050044 .
m_ovality 3.387e+02 1.789e+02 1.893 0.061576 .
hw.vfrac -3.649e+00 2.202e+00 -1.657 0.100962
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.248 on 89 degrees of freedom
Multiple R-squared: 0.66, Adjusted R-squared: 0.637
F-statistic: 28.79 on 6 and 89 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + m_ovality + m_taper + m_volume +
m_whorliness + m_sweep1 + hw.vfrac, data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.29778 -0.18770 -0.08215 0.20673 0.51244
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.689e+01 2.516e+00 -10.688 8.61e-07 ***
SWV 7.296e-03 5.162e-04 14.135 6.18e-08 ***
density 1.370e-02 1.768e-03 7.748 1.56e-05 ***
m_ovality 3.858e+02 8.996e+01 4.289 0.00159 **
m_taper -8.456e-02 2.654e-02 -3.187 0.00971 **
m_volume 1.685e+00 6.502e-01 2.592 0.02686 *
m_whorliness 1.099e+00 3.989e-01 2.756 0.02028 *
m_sweep1 -2.341e-01 1.422e-01 -1.646 0.13079
hw.vfrac -1.814e+00 1.660e+00 -1.092 0.30025
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3199 on 10 degrees of freedom
Multiple R-squared: 0.9788, Adjusted R-squared: 0.9619
F-statistic: 57.78 on 8 and 10 DF, p-value: 2.26e-07
|
Call:
lm(formula = y ~ SWV + m_a0 + weight + m_sweep1 + m_ovality +
m_a1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.28074 -0.08097 -0.00794 0.06625 0.42431
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.509e+00 2.814e-01 5.361 6.58e-07 ***
SWV -1.081e-04 6.759e-05 -1.599 0.1135
m_a0 -2.265e-03 1.168e-03 -1.939 0.0557 .
weight 6.249e-04 4.242e-04 1.473 0.1443
m_sweep1 5.241e-02 2.294e-02 2.285 0.0247 *
m_ovality -3.539e+01 1.939e+01 -1.825 0.0714 .
m_a1 8.768e-04 5.197e-04 1.687 0.0951 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1351 on 88 degrees of freedom
Multiple R-squared: 0.2593, Adjusted R-squared: 0.2088
F-statistic: 5.134 on 6 and 88 DF, p-value: 0.0001451
|
Call:
lm(formula = y ~ m_sed + m_led + m_ovality + m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.14159 -0.06416 -0.02260 0.01966 0.39860
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.206767 0.184817 6.530 1.34e-05 ***
m_sed -0.005454 0.001974 -2.763 0.0153 *
m_led 0.004393 0.001791 2.453 0.0279 *
m_ovality -61.808328 35.285081 -1.752 0.1017
m_whorliness -0.235371 0.140462 -1.676 0.1160
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.132 on 14 degrees of freedom
Multiple R-squared: 0.4577, Adjusted R-squared: 0.3028
F-statistic: 2.954 on 4 and 14 DF, p-value: 0.05797
|
Call:
lm(formula = y ~ density + sweep.prod + m_waist + m_a2, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.2187 -1.4615 -0.2139 1.0972 10.3181
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.094252 2.899470 4.171 6.14e-05 ***
density -0.008411 0.003038 -2.768 0.00663 **
sweep.prod 2.042667 0.857806 2.381 0.01900 *
m_waist -38.089688 25.865324 -1.473 0.14376
m_a2 0.404822 0.283115 1.430 0.15564
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.165 on 108 degrees of freedom
Multiple R-squared: 0.1638, Adjusted R-squared: 0.1328
F-statistic: 5.288 on 4 and 108 DF, p-value: 0.0006287
|
Call:
lm(formula = y ~ 1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-2.8139 -1.3834 -0.9012 0.4361 11.3666
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.258 0.649 8.102 9.58e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.974 on 20 degrees of freedom
|
Call:
lm(formula = y ~ density + m_sed + m_a1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-6.3515 -2.2370 -0.7273 1.3997 14.1913
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.207336 4.377100 3.246 0.00156 **
density -0.014262 0.004726 -3.018 0.00318 **
m_sed 0.012210 0.004824 2.531 0.01283 *
m_a1 0.021119 0.010744 1.966 0.05192 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.338 on 107 degrees of freedom
Multiple R-squared: 0.1264, Adjusted R-squared: 0.1019
F-statistic: 5.159 on 3 and 107 DF, p-value: 0.002275
|
Call:
lm(formula = y ~ 1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.6944 -2.4611 -0.2111 1.0389 13.3056
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.2944 0.8281 7.601 2.55e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.795 on 20 degrees of freedom
|
Call:
lm(formula = y ~ SWV + m_waist + m_a2, data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.4509 -1.5887 -0.5386 1.0625 10.2911
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.942566 3.582819 3.333 0.00123 **
SWV -0.002244 0.001086 -2.066 0.04159 *
m_waist -53.844271 31.306135 -1.720 0.08874 .
m_a2 0.574311 0.342203 1.678 0.09661 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.52 on 94 degrees of freedom
Multiple R-squared: 0.09871, Adjusted R-squared: 0.06995
F-statistic: 3.432 on 3 and 94 DF, p-value: 0.02015
|
Call:
lm(formula = y ~ SWV + m_a0 + m_volume + m_sed + weight + m_a1 +
m_led + m_taper + m_waist + m_a2, data = LL)
Residuals:
Min 1Q Median 3Q Max
-1.7645 -0.5759 0.2205 0.7094 1.3483
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.517e+01 1.926e+01 4.423 0.00166 **
SWV -1.389e-02 3.555e-03 -3.906 0.00359 **
m_a0 -1.530e-01 1.268e-01 -1.207 0.25837
m_volume 1.219e+02 3.323e+01 3.669 0.00516 **
m_sed 2.630e-01 7.630e-02 3.447 0.00731 **
weight -7.660e-02 2.161e-02 -3.545 0.00626 **
m_a1 -1.666e+00 7.629e-01 -2.183 0.05688 .
m_led -2.719e-01 7.152e-02 -3.802 0.00420 **
m_taper 8.904e+00 3.706e+00 2.403 0.03972 *
m_waist -3.233e+02 7.726e+01 -4.185 0.00236 **
m_a2 1.895e+00 8.199e-01 2.312 0.04610 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.378 on 9 degrees of freedom
Multiple R-squared: 0.8949, Adjusted R-squared: 0.7782
F-statistic: 7.667 on 10 and 9 DF, p-value: 0.002621
|
Call:
lm(formula = y ~ SWV + density + m_whorliness + m_taper, data = LL)
Residuals:
Min 1Q Median 3Q Max
-8.5526 -2.1948 -0.5343 1.6360 15.9240
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 50.677145 8.169233 6.203 1.04e-08 ***
SWV -0.006277 0.001527 -4.112 7.67e-05 ***
density -0.016368 0.005161 -3.172 0.00197 **
m_whorliness -2.400092 1.140879 -2.104 0.03772 *
m_taper -0.233965 0.125925 -1.858 0.06590 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.566 on 108 degrees of freedom
Multiple R-squared: 0.1962, Adjusted R-squared: 0.1664
F-statistic: 6.589 on 4 and 108 DF, p-value: 8.771e-05
|
Call:
lm(formula = y ~ SWV + m_led + m_a1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-5.4833 -1.2486 -0.4416 1.9113 4.4711
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 86.50366 19.50337 4.435 0.000363 ***
SWV -0.01948 0.00460 -4.236 0.000557 ***
m_led -0.03015 0.01243 -2.426 0.026685 *
m_a1 -0.04164 0.02580 -1.614 0.125005
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.824 on 17 degrees of freedom
Multiple R-squared: 0.5505, Adjusted R-squared: 0.4711
F-statistic: 6.939 on 3 and 17 DF, p-value: 0.002966
|
Call:
lm(formula = y ~ SWV + m_ovality + density + m_whorliness + m_a1,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-9.4893 -3.3069 -0.5184 2.3588 17.7155
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.634e+01 1.173e+01 4.802 5.24e-06 ***
SWV -7.652e-03 2.182e-03 -3.507 0.000668 ***
m_ovality 1.873e+03 7.077e+02 2.646 0.009390 **
density -1.829e-02 7.345e-03 -2.490 0.014350 *
m_whorliness -2.890e+00 1.624e+00 -1.780 0.077937 .
m_a1 -2.640e-02 1.735e-02 -1.522 0.131065
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.082 on 105 degrees of freedom
Multiple R-squared: 0.1844, Adjusted R-squared: 0.1456
F-statistic: 4.749 on 5 and 105 DF, p-value: 0.0005952
|
Call:
lm(formula = y ~ SWV + m_ovality + m_whorliness + m_taper, data = LL)
Residuals:
Min 1Q Median 3Q Max
-8.5884 -3.6210 0.4863 2.8844 9.1941
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.624e+01 2.236e+01 3.410 0.00359 **
SWV -2.094e-02 5.895e-03 -3.552 0.00265 **
m_ovality 2.646e+03 1.412e+03 1.874 0.07936 .
m_whorliness 9.132e+00 5.657e+00 1.614 0.12601
m_taper -5.635e-01 3.774e-01 -1.493 0.15493
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.448 on 16 degrees of freedom
Multiple R-squared: 0.5543, Adjusted R-squared: 0.4429
F-statistic: 4.975 on 4 and 16 DF, p-value: 0.00846
|
Call:
lm(formula = y ~ SWV + density + m_taper, data = LL)
Residuals:
Min 1Q Median 3Q Max
-9.946 -3.105 -0.955 2.327 29.900
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 75.091423 14.652257 5.125 1.59e-06 ***
SWV -0.010613 0.002732 -3.884 0.000191 ***
density -0.027779 0.009864 -2.816 0.005921 **
m_taper -0.460498 0.208543 -2.208 0.029664 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.96 on 94 degrees of freedom
Multiple R-squared: 0.1844, Adjusted R-squared: 0.1584
F-statistic: 7.083 on 3 and 94 DF, p-value: 0.0002418
|
Call:
lm(formula = y ~ SWV + m_a0 + hw.vfrac + m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-8.4976 -3.4642 0.1459 3.1434 15.1500
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 135.928624 33.825081 4.019 0.00112 **
SWV -0.025060 0.008128 -3.083 0.00757 **
m_a0 -0.083429 0.024781 -3.367 0.00424 **
hw.vfrac -38.914741 22.744306 -1.711 0.10768
m_whorliness -9.543790 6.440547 -1.482 0.15909
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.898 on 15 degrees of freedom
Multiple R-squared: 0.5262, Adjusted R-squared: 0.3998
F-statistic: 4.164 on 4 and 15 DF, p-value: 0.01826
|
Call:
lm(formula = y ~ SWV + m_led + density + m_sed, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.4416 -1.2848 0.0444 1.0252 4.1047
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.2602020 3.5839449 4.258 4.42e-05 ***
SWV -0.0041115 0.0006817 -6.031 2.32e-08 ***
m_led -0.0285152 0.0097001 -2.940 0.00402 **
density 0.0071756 0.0023647 3.035 0.00302 **
m_sed 0.0155077 0.0104349 1.486 0.14016
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.634 on 108 degrees of freedom
Multiple R-squared: 0.4176, Adjusted R-squared: 0.396
F-statistic: 19.36 on 4 and 108 DF, p-value: 4.946e-12
|
Call:
lm(formula = y ~ SWV + m_led + m_whorliness + hw.vfrac, data = LL)
Residuals:
Min 1Q Median 3Q Max
-1.8945 -0.5266 -0.1464 0.9082 1.8528
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 30.459560 6.808582 4.474 0.000384 ***
SWV -0.005985 0.001618 -3.698 0.001949 **
m_led -0.014924 0.004404 -3.389 0.003745 **
m_whorliness -2.370611 1.210732 -1.958 0.067903 .
hw.vfrac -5.814268 4.201633 -1.384 0.185418
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.145 on 16 degrees of freedom
Multiple R-squared: 0.5512, Adjusted R-squared: 0.439
F-statistic: 4.913 on 4 and 16 DF, p-value: 0.008902
|
Call:
lm(formula = y ~ SWV + m_led + density + m_sed + m_sweep1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.576 -1.782 -0.099 1.494 5.508
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.3355124 5.0898748 2.620 0.010071 *
SWV -0.0039981 0.0009606 -4.162 6.40e-05 ***
m_led -0.0527561 0.0137747 -3.830 0.000216 ***
density 0.0138311 0.0033452 4.135 7.09e-05 ***
m_sed 0.0364908 0.0148036 2.465 0.015292 *
m_sweep1 -0.5561260 0.3520536 -1.580 0.117136
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.302 on 107 degrees of freedom
Multiple R-squared: 0.4114, Adjusted R-squared: 0.3839
F-statistic: 14.96 on 5 and 107 DF, p-value: 4.049e-11
|
Call:
lm(formula = y ~ SWV + m_led + m_whorliness + m_taper + m_sed +
m_a1 + m_a2 + hw.vfrac, data = LL)
Residuals:
Min 1Q Median 3Q Max
-1.2775 -0.5499 -0.2484 0.8138 1.9334
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 40.475169 11.009177 3.676 0.00317 **
SWV -0.008303 0.002532 -3.278 0.00660 **
m_led 0.072152 0.043742 1.650 0.12496
m_whorliness -2.805539 1.246491 -2.251 0.04394 *
m_taper -6.822035 2.695447 -2.531 0.02637 *
m_sed -0.084517 0.039825 -2.122 0.05532 .
m_a1 1.325251 0.567780 2.334 0.03779 *
m_a2 1.302610 0.567042 2.297 0.04040 *
hw.vfrac -7.669701 4.630325 -1.656 0.12353
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.171 on 12 degrees of freedom
Multiple R-squared: 0.8209, Adjusted R-squared: 0.7015
F-statistic: 6.876 on 8 and 12 DF, p-value: 0.001684
|
3 Conclusions
Best off using a dynamic estimate of board stiffness based on manual hitman velocity and CHH total density, but other measures of board stiffness (including Metriguard CLT) give similar results.
Green log SWV alone very poorly predicts average dry board stiffness.
Distance from pith at LE has limitations as a predictor for average distance from the pith throughout a board.
The saw pattern plots above indicate that there are a lot of boards missing from the dataset. While in routine operation this is ineveitable (and hence an unaviodable if log performance is to be predicted) in a research context this adds extra confusion owing to the boards being missing to different degrees in different logs and the missing boards being clustered (rather than randomly distributed) within individual logs.
4 Todo
- other approaches to finding ‘best’ model. DONE
- models for ‘good’ subsets. DONE
- models for inner/outer boards. DONE